Remote vibration detection of submerged equipment using magnetic field sensing

ABSTRACT

Techniques for operating a sensor are provided. An example method according to these techniques includes sensing, at the sensor, changes in intensity of a magnetic field of a magnet affixed to a monitored asset to produce sensor data, wherein the monitored asset is disposed in a non-metallic liquid or solid medium, and wherein the sensor is disposed outside of the non-metallic medium; The method also includes analyzing, at the sensor, the sensor data to produce feature information indicative of vibration of the monitored asset. The method also includes providing the feature information to a predictive algorithm to generate prognosis information indicating an occurrence of a known condition of the monitored asset.

BACKGROUND

Sensor technology has enabled sensors to be deployed in various settingswhere direct monitoring of assets may be difficult or impractical.Wireless sensor technology has made it possible to collect vast amountsof information about these assets. However, utilizing this informationto improve the function of the monitored assets has proved to bechallenging.

SUMMARY

A example method for operating a sensor according to the disclosureincludes sensing, at the sensor, changes in intensity of a magneticfield of a magnet affixed to a monitored asset to produce sensor data,wherein the monitored asset is disposed in a non-metallic liquid orsolid medium, and wherein the sensor is disposed outside of thenon-metallic medium; analyzing, at the sensor, the sensor data toproduce feature information indicative of vibration of the monitoredasset; and providing the feature information to a predictive algorithmto generate prognosis information indicating an occurrence of a knowncondition of the monitored asset.

Implementations of such a method may include one or more of thefollowing features. The method includes determining a velocity of themonitored asset based on the signal value. The sensor is a first sensor,wherein the magnet is a first magnet, wherein a second magnet is affixedto the monitored asset, and the sensing changes in intensity of amagnetic field of a magnet affixed to a monitored asset includes sensingchanges in intensity of a first magnetic field associated with the firstmagnet using the first sensor, and sensing changes in intensity of asecond magnetic field associated with the second magnet using a secondsensor. The method includes determining a first electrical signal valuebased on the changes in intensity of the first magnetic field;determining a second electrical signal value based on based on changesin intensity of the second magnetic field; determining a first velocityof the monitored asset along a first axis based on the first electricalsignal value; and determining a second velocity of the monitored assetalong a second axis based on the second electrical signal value. Themethod includes determining a phase relationship between the firstelectrical signal value and the second electrical signal value. Themethod includes determining an expected value for each of the one ormore features over time. The method includes identifying a deviationfrom the expected value for a feature of the one or more features, andsending the feature information for the feature for which the deviationwas identified to a server.

An example monitoring system according to the disclosure includes asensor, a wireless transceiver, and a processor. The sensor isconfigured to changes in intensity of a magnetic field of a magnetaffixed to a monitored asset to produce sensor data. The monitored assetis disposed in a non-metallic liquid or solid medium, and the monitoringsystem is disposed outside of the non-metallic liquid or solid medium.The wireless transceiver configured to transmit data to and receive datafrom a server via a communication network. The processor is configuredto analyze the sensor data to produce feature information indicative ofvibration of the monitored asset, and provide the feature data to apredictive algorithm to generate prognosis information indicating anoccurrence of a known condition of the monitored asset. The processor isfurther configured to determine a signal value based on the changes inintensity of the magnetic field. The processor is further configured todetermine a velocity of the monitored asset based on the signal value.The sensor is a first sensor, the magnet is a first magnet, a secondmagnet is affixed to the monitored asset, and the processor beingconfigured to sense changes in intensity of a magnetic field of a magnetaffixed to a monitored asset further is further configured to sensechanges in intensity of a first magnetic field associated with the firstmagnet using the first sensor, and sense changes in intensity of asecond magnetic field associated with the second magnet using a secondsensor. The processor is further configured to determine a firstelectrical signal value based on the changes in intensity of the firstmagnetic field, determine a second electrical signal value based onbased on changes in intensity of the second magnetic field, determine afirst velocity of the monitored asset along a first axis based on thefirst electrical signal value; and determine a second velocity of themonitored asset along a second axis based on the second electricalsignal value. The processor is further configured to determine a phaserelationship between the first electrical signal value and the secondelectrical signal value. The processor is further configured todetermine an expected value for each of the one or more features overtime. The processor is further configured to identify a deviation fromthe expected value for a feature of the one or more features, and tosend the feature information for the feature for which the deviation wasidentified to a server.

An example non-transitory, computer-readable medium according to thedisclosure, having stored thereon computer-readable instructionsoperating for operating a monitoring system. The instructions comprisinginstructions configured to cause the monitoring system to sense changesin intensity of a magnetic field of a magnet affixed to a monitoredasset to produce sensor data, the monitored asset is disposed in anon-metallic liquid or solid medium, and the sensor is disposed outsideof the non-metallic medium; analyze the sensor data to produce featureinformation indicative of vibration of the monitored asset; and providethe feature data to a predictive algorithm to generate prognosisinformation indicating an occurrence of a known condition of themonitored asset.

Implementations of such a non-transitory, computer-readable medium mayinclude one or more of the following features. The instructionsconfigured to cause the monitoring system to determine the vibrationdata include instructions configured to cause the monitoring system todetermine a signal value based on the changes in intensity of themagnetic field. The medium includes instructions configured to cause themonitoring system to determine a velocity of the monitored asset basedon the signal value. The sensor is a first sensor, the magnet is a firstmagnet, a second magnet is affixed to the monitored asset, and theinstructions configured to cause the monitoring system to sense changesin intensity of a magnetic field of a magnet affixed to a monitoredasset further comprise instructions configured to cause the monitoringsystem to sense changes in intensity of a first magnetic fieldassociated with the first magnet using the first sensor, and sensechanges in intensity of a second magnetic field associated with thesecond magnet using a second sensor. The medium includes instructionsconfigured to cause the monitoring system to determine a firstelectrical signal value based on the changes in intensity of the firstmagnetic field, determine a second electrical signal value based onbased on changes in intensity of the second magnetic field, determine afirst velocity of the monitored asset along a first axis based on thefirst electrical signal value, and determine a second velocity of themonitored asset along a second axis based on the second electricalsignal value. The medium includes instructions configured to cause themonitoring system to determine a phase relationship between the firstelectrical signal value and the second electrical signal value. Themedium including instructions configured to cause the monitoring systemto determine an expected value for each of the one or more features overtime. The medium including instructions configured to cause themonitoring system to identify a deviation from the expected value for afeature of the one or more features, and send the feature informationfor the feature for which the deviation was identified to a server.

Another example method for operating a sensor according to thedisclosure includes sensing changes in intensity of a magnetic field ofa magnet affixed to a monitored asset to produce sensor data, whereinthe monitored asset is disposed in a non-metallic liquid or solidmedium, and wherein the sensor is disposed outside of the non-metallicmedium; and determining vibration data for the monitored asset byanalyzing the changes in intensity of the magnetic field of the magnetassociated with the monitored asset.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a block diagram of an example operating environment that maybe used to implement various techniques disclosed herein.

FIG. 2 is a block diagram that provides additional details of several ofthe components illustrated in the example operating environment of FIG.1.

FIG. 3 is a block diagram of an example of a remote sensor shown in FIG.1.

FIG. 4 is a block diagram of an example of a server shown in FIG. 1.

FIG. 5 is a block diagram of an example of a user device shown in FIG.1.

FIG. 6 is a flow diagram of an example process for monitoring an assetand for generating a prognosis for the monitored asset.

FIG. 7 is a flow diagram of another example process for monitoring anasset and for generating a prognosis for the monitored asset.

FIG. 8 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.7.

FIG. 9 is a flow diagram of an example process for determining issueresolution information.

FIG. 10 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.7.

FIG. 11 is a flow diagram of an example process for training apredictive algorithm with feature information tagged with repairinformation.

FIG. 12 is a flow diagram of an example process for training apredictive algorithm with feature information tagged with repairinformation.

FIG. 13 is a flow diagram of an example process for obtaining serviceinformation for a monitored asset.

FIG. 14 is a flow diagram of an example process for associating aconfidence level with the prognosis information according to thedisclosure.

FIG. 15 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.14.

FIG. 16 is a diagram of an example of an additional stage that may beused to implement, at least in part, stage 704 of the processillustrated in FIG. 7.

FIG. 17 is a flow diagram of an example process for operating amonitoring system.

FIG. 18 is a flow diagram of an example process for modifying theoperating parameters of a sensor.

FIG. 19 is a flow diagram of an example process for operating amonitoring system.

FIG. 20 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.18.

FIG. 21 is a diagram of an example of an additional stage that may beused to implement at least in part, stage 1702 of the processillustrated in FIG. 17.

FIG. 22 is a flow diagram of an example process for modifying theoperating parameters of a sensor.

FIG. 23 is an example state diagram illustrating an example of variousstates that a sensor may transition between.

FIG. 24 is a block diagram of an example operating environment that canbe used to implement various techniques disclosed herein.

FIG. 25 is a block diagram of an example operating environment that canbe used to implement various techniques disclosed herein.

FIG. 26 is a block diagram of an example operating environment that canbe used to implement various techniques disclosed herein.

FIG. 27 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 28 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 29 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 30 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 31 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 32 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

FIG. 33 is a flow diagram of an example process for operating amonitoring system according to the disclosure.

Like reference symbols in the various drawings indicate like elements,in accordance with certain example implementations.

DETAILED DESCRIPTION

Techniques for remotely sensing a monitored asset using changes to amagnetic field associated with the monitored asset are provided. Forexample, the monitored asset in techniques disclosed herein can beconfigured to operate in a non-ferromagnetic medium. The medium can be aliquid, semiliquid, semisolid, or solid. Monitoring of a monitored asset101 that is submerged in a liquid, semiliquid, or semisolid can beproblematic. Affixing a sensor to the monitored asset 101 thatcommunicates sensor data to the sensor 102 using radio frequencies (RF)is generally not feasible for situations where the sensor is submergedin a liquid. RF signals attenuate rapidly in liquids like water, andthus may not work for monitoring submerged monitored assets 101. RFsignals also attenuate more quickly in solids, semisolids, andsemiliquid environments than in air. Wired vibration sensors could bemounted on submerged monitored asset 101. However, the complexity ofproviding a separate source of power to the installed sensors, as wellas bringing measurement data from the sensors, has long preventedattempts to characterize the health and performance of such equipment.The wires for providing power to the sensor and for receiving data fromthe sensor may need to be shielded from intrusion by the medium in whichthe pump is submerged. The fluid may also be corrosive and may degradesuch shielding over time.

Implementations where the monitored asset is disposed in a solidmaterial with the monitored asset also present significant challenges.Replacing or servicing the sensor may require digging up the solidmedium in which the sensor and the monitored asset are disposed.

Techniques disclosed herein have a sensor disposed outside of a mediumin which a monitored asset is disposed, and one or more magnets affixedto the monitored asset. The sensor is configured to sense changes inintensity of the magnetic field(s) associated with the one or moremagnets. Sensing the changes in intensity of the magnetic field can beused to monitor vibration of the monitored asset. As the monitored assetvibrates, the position of the magnet affixed to the monitored asset alsochanges, and the sensor(s) of the sensor can be configured to detectchanges in intensity of the magnetic field produced by the magnet(s)affixed to the monitored asset. The sensor can derive vibrationinformation for the monitored asset from the sensor data produced by thesensor(s) of the sensor. This vibration data can be used by the sensorto characterize behavior of the monitored asset and to extract featureinformation from the sensor data that may be indicative of a problemwith the monitored asset. The feature information can be sent to aserver of the monitoring system which is configured to analyze thefeature information using one or more predictive algorithm(s) to produceprognosis information for the monitored asset. The prognosis informationcan be indicative of an issue associated with the monitored asset forwhich repair or maintenance may be desirable.

FIG. 1 is a block diagram of an example operating environment 100 inwhich a monitoring system according to the disclosure may beimplemented. The monitoring system includes a front-end portion 110 anda back-end portion 111. The front-end portion 110 in this exampleimplementation includes one or more monitored assets, such as amonitored asset 101 a and a monitored asset 101 b, and one or moresensors, such as a sensor 102 a, a sensor 102 b, and a sensor 102 c. Thefront-end portion 110 may also include a network device 104. Theback-end portion 111 of the monitoring system may include a server, suchas a server 106. The front-end portion 110 of the monitoring system maybe remotely located from the back-end portion 111 of the monitoringsystem, and the front-end portion 110 and the back-end portion 111 maybe communicably coupled with a network, such as network 105. Theoperating environment 100 may also include a user device 107 that isconfigured to communicate with the server 106 via the network.

The front-end portion 110 of the monitoring system may be located at alocation where one or more monitored assets, such as the monitored asset101 a and the monitored asset 101 b, are located. The example operatingenvironment 100 includes two monitored assets. Other implementations mayinclude a single monitored asset or may include more than two monitoredassets. Furthermore, the monitored assets may include more than onemonitored asset of the same type or a combination of multiple types ofmonitored assets. A monitored asset may be any type of object for whichsensor data may be collected. In some implementations, the monitoredasset may not require a separate sensor and can be configured to provideinformation to the server which can be used to determine a prognosis.

A monitored asset may comprise a piece of equipment or machinery, or acomponent thereof, for which sensor data may be collected and for whichprognosis information identifying an occurrence of a possible conditionof the monitored asset may be determined. Some examples of suchmonitored assets include, but are not limited to, motors, generators,pumps, valves, components of heating, ventilation, and air conditioning(HVAC) systems, gas turbines, wind turbines, mining or loggingequipment, and process equipment used by the refining and processingindustries to convert raw materials into products or refined materials.A monitored asset may also be other types of objects that may bemonitored and for which remote monitoring may be desirable. For example,the techniques disclosed herein could be used to monitor agriculturalassets, such as a crops or livestock. The techniques disclosed hereincould also be used to monitor the status of infrastructural elements,such as road, traffic signals, bridges, tunnels, buildings, or othersuch infrastructural elements. These techniques could also be used tomonitor natural features, such as a lake, a stream, plant life, oranimal life for which sensor data may be collected and analyzed toidentify an occurrence of a condition of the monitored asset. Theseexamples are provided to illustrate the flexibility of the techniquesdisclosed herein and do not limit these techniques to these specifictypes of monitored assets.

Each monitored asset 101 may be associated with one or more sensors 102.A sensor 102 may sense one or more characteristics associated with amonitored asset and to produce sensor data. In this example, monitoredasset 101 a is associated with sensor 102 a, and monitored asset 101 bis associated with sensor 102 b and the sensor 102 c. A sensor 102 maycomprise more than one type of sensor (e.g., a vibration sensor and atemperature sensor). Other types of sensors, such as but not limited tomagnetometers, accelerometers, gyroscopes, visible light sensors,infrared sensors, ultraviolet sensors, temperature sensors, fluidsensors, pressure sensors, optical sensors, radiation sensors, vibrationsensors, chemical sensors, acoustic sensors other types of sensors, or acombination thereof, may be utilized depending on the type of monitoredasset 101 for which sensor data are being collected.

The sensor(s) 102 may extract feature information from the sensor dataand to communicate the sensor data, the feature information, or both tothe server 106. The sensor(s) 102 may determine, at least in part, whichfeatures to extract from the sensor data. The sensor 102 may performfeature extraction in which the sensor data obtained by one or moresensor(s) of the sensor 102 are analyzed to produce information that maybe used by one or more predictive algorithms utilized by the server 106to discriminate between possible conditions of a monitored asset. Forexample, the features may serve as classification variables that may beused by a classification aspect of the predictive algorithms to identifythe occurrence or impending occurrence of a possible condition of themonitored asset 101. The values of various features or combinationsthereof may be indicative of the different possible conditions for whichthe predictive algorithms have been trained.

The sensor(s) 102 may wirelessly communicate with the network device104. The network device 104 may relay raw sensor data, processed sensordata (e.g., the feature information), or a combination thereof to theserver 106. The network device 104 may also be configured to relayconfiguration information from the server 106 to the sensor(s) 102. Thenetwork device 104 may comprise a router, a wireless access point, awireless base station, or other device configured to wirelesslycommunicate with the sensor 102 a, the sensor 102 b, and the sensor 102c. The network device 104 may be operated by an owner or operator of themonitored assets or may be operated by a third party, such as a networkservice provider. The network device 104 may be connected to a backhaulconnection to a network, such as the network 105. The network 105 maycomprise one or more public networks, one or more private networks, or acombination thereof. The network 105 may be, at least in part, the setof interconnected networks referred to as the Internet.

The sensor(s) 102 may be powered by a battery and/or other onboard powersource. The sensor(s) 102 may send the feature information, but not thesensor data, to the server 106, which may conserve power and/or reducethe amount of network bandwidth used to transmit data to the server 106.The sensor(s) 102 may send the sensor data to the server 106 in responseto a request for the sensor data from the server 106. The sensor(s) 102may store sensor data, feature information, or both for at least aperiod of time and to provide the sensor data to the server 106 ondemand.

The sensors of the sensor(s) 102 may produce a large amount of data thatmay be processed and used to produce prognosis information for themonitored asset 101. The processing of the sensor data may bedistributed between the sensor(s) 102 and the server 106 by having thefeature information extracted from the sensor data at the sensor(s) 102.This approach may significantly reduce the amount of data that thesensor(s) transmits to the server 106. The feature information isderived from the sensor data and may be significantly smaller in sizethan the sensor data. Thus, the sensor(s) 102 may not use high-capacitytransmission links to send data from the sensor(s) 102 to the server106. The sensor(s) 102 may include low-power wireless transmitters thatmay provide long-range wireless data transmissions.

The sensor(s) 102 may comprise Internet of Things (IoT) devices that areconfigured to be deployed in an operating environment, such as thatillustrated in FIG. 1, and to wirelessly communicate with the server 106and/or other networked components of the monitoring system. IoT providesa framework for internetworking devices such as the sensor(s) 102 andprovides a means for automating the monitoring of assets, such as themonitored asset(s) 101 illustrated in FIG. 1. The sensors(s) 102 maysupport one or more IoT communications protocols, including but notlimited to the Enhanced Machine-Type Communication (eMTC) protocol, theNarrowband Internet of Things (NB-IoT) protocol, and/or the LightweightMachine-to-Machine (LwM2M) protocol. The sensors(s) 102 utilize one ormore IoT communications protocols to send sensor data, featureinformation extracted from the sensor data, other information, or acombination thereof to the server 106. The server 106 may use one ormore IoT protocols to send communication control signals, sensorconfiguration information, or a combination thereof to the sensor(s)102. The server 106 may send sensor configuration information to thesensor(s) 102 that facilitates the reconfiguration of operatingparameters of the sensor(s) 102 without requiring the sensor(s) 102already disposed in an operating environment to be manually reconfiguredor replaced.

The server 106 may use one or more predictive algorithms to make adetermination that a condition has or is likely to occur with amonitored asset 101 and to produce prognosis information for themonitored asset 101. The server 106 may make this determination based onfeature information collected by the sensor(s) 102 associated with themonitored asset 101. While referred to herein as predictive algorithms,this includes the use of a single algorithm, and the algorithms mayinclude both predictive and classification aspects. The classificationaspect may include, for example, making a determination whether featureinformation obtained for a monitored asset is indicative of one of adiscrete number of possible conditions that the predictive algorithmshave been trained to identify as having occurred or the occurrence ofwhich is imminent. The predictive aspect may include, for example,making a determination as to a likelihood that the monitored asset willfail or malfunction, and/or likelihood that the condition of themonitored asset 101 will deteriorate. The predictive aspect is notlimited to these particular examples and may determine a likelihood ofthe occurrence of other events related to the monitored asset 101. Theprognosis information generated for the monitored asset may includeinformation provided by the classification aspect, the predictiveaspect, or both. The output of the classification aspect may be used toaid diagnosis of a condition of the monitored asset 101 by identifyingthat a possible condition has occurred or is imminent (e.g., within afuture threshold amount of time) based on the feature informationcollected by the sensor(s) 102.

The server 106 may obtain feature information associated with sensordata associated with a monitored asset, and to analyze the featureinformation using the predictive algorithms to produce prognosisinformation. The predictive algorithms used by the server 106 may betrained using feature information that has been “tagged” or labeled toindicate that a certain possible condition of the monitored asset 101 ispresent. A possible condition is a state of the monitored asset forwhich the predictive algorithms have been trained to recognize usingtagged feature information which has been associated with a labelidentifying the possible condition. The tagged data may include valuesfor one or more of the features indicative of the occurrence of thatcondition. Once the predictive algorithms have been trained, thepredictive algorithms may be provided with untagged data. The predictivealgorithms may be used to analyze the untagged data using regression,classification, neural networks, deep learning, machine learning, orother techniques, or a combination thereof to produce prognosisinformation for the monitored asset for which the untagged featureinformation was obtained.

The prognosis information produced by the server 106 may includeinformation indicative of an occurrence of a possible condition of themonitored asset. The server 106 may also be configured to receiveservice information from a user in response to the prognosisinformation. The service information may include diagnostic information,repair information, or both that may be used by the server 106 toconfirm whether the prognosis information was correct. Having a uservisit the monitored asset and provide an assessment whether theprognosis information produced by the predictive algorithm is correct oraccurate may provide feedback that may be used to refine the predictivealgorithms used by the server 106. The examples that follow will discussexample types of information that the user may provide and how thisinformation may be used to influence the predictive algorithms used bythe server 106. The training of the predictive algorithms may be revisedbased on the service information or other information that is collectedduring operation of the monitoring system. The server 106 may tagfeature information that was used to generate the prognosis informationin response to the service information indicating that the prognosisinformation was correct. The service information may be included in thetagged feature information that is used to refine the predictivealgorithms used by the server. The tagged feature information caninclude diagnostic information provided by the user, repair informationprovided by the user, or both. This continued refinement of thepredictive algorithms is discussed in greater detail in the exampleimplementations that follow.

The server 106 may transmit prognosis information for a monitored assetto the user device 107. The user device 107 may be a computing deviceassociated with a user. The user device 107 may be communicably coupledwith the server 106 via the network 105. The user device 107 may be, forexample, a smartphone, a tablet computing device, laptop, or otherportable computing device. The user device 107 may also be stationary orsubstantially stationary, such as a desktop computing device.

The server 106 may transmit the prognosis information for the monitoredasset to the user device 107 as a work order or other request to have auser of the user device 107 check on the monitored asset. The user maybe a technician or other person who may physically check on themonitored asset to verify whether the prognosis information produced bythe server 106 is correct. The user may provide service information tothe server 106 via the user device 107. The service information mayinclude diagnostic information, repair information, or both. Thediagnostic information may include information indicative of whether theprognosis information produced by the server 106 for the monitored assetwas correct. The diagnostic information may include an indication of anactual condition of the monitored asset, e.g., if the prognosisinformation was incorrect. The indication of the actual condition may beused to refine the predictive algorithms to better predict the conditionof the monitored asset based on the sensor data (e.g., to better predictthe condition of the monitored asset when presented in the future withsensor data that are similar to the sensor data that resulted in anincorrect prediction this time). The repair information may compriseinformation indicative of repairs or maintenance performed on themonitored asset in response to the prognosis information.

The server 106 may utilize the service information to confirm whetherthe prognosis information for the monitored asset was correct. Theserver 106 may also be configured to determine condition resolutioninformation for the monitored asset. The condition resolutioninformation may be determined by obtaining additional sensor dataassociated with the monitored asset from the sensor(s) associated withthe monitored asset, and whether the condition of the monitored assethas been resolved.

The server 106 may refine the predictive algorithms used to generate theprognosis information. One scenario where the predictive algorithms maybe refined is when the diagnostic information, repair information, orboth provided by the user confirms the prognosis information produced bythe predictive algorithms. In this scenario, the predictive algorithmsappear to have correctly identified the occurrence of a possiblecondition of the monitored asset 101 in untagged feature informationobtained for the monitored asset 101. The server 106 may tag theformerly untagged feature information to indicate that the featureinformation is indicative of the occurrence of the possible condition.The diagnostic information, the repair information, or both provided bythe user may also be included with the tagged data. The tagged featureinformation may be used by the server 106 to refine the training of thepredictive algorithms.

Another such scenario where the predictive algorithm(s) may be refinedis when the diagnostic information, the repair information, or bothprovided by the user does not support or contradicts the prognosisinformation produced by the predictive algorithm(s). The prognosisinformation may be incorrect, the diagnostic information or repairinformation may be incorrect, or a combination thereof. The predictivealgorithm(s) may have incorrectly identified the condition of themonitored asset. The user may also have misdiagnosed the condition ofthe monitored asset and may have made incorrect or unnecessary repairs.Where the diagnosis information, the repair information, or bothcontradict the prognosis information, the server 106 may assess whetherto adopt the prognosis information and to disregard or discount thediagnostic information.

The server 106 may associate a confidence level with the prognosisinformation, for example, based in part on historical data collected forthe monitored asset. The server 106 may disregard or discount (e.g., notcompletely disregard, but use a weighting between 1.0 and 0) serviceinformation provided by the user that is contrary to the prognosisinformation if the confidence level associated with the prognosisinformation exceeds a predetermined threshold. The server 106 maydisregard or discount the service information by not including thisuser-provided information when producing tagged feature information fortraining the predictive algorithms, or by including the serviceinformation but applying a weighting to the service information (and/orapplying a weighting to the prognosis information). The confidence levelassociated with the prognosis information may also be based at least inpart on historical data collected for other monitored assets of a sameor similar type of asset as the monitored asset for which the prognosisinformation was generated. The server 106 may also be configured toassociate a confidence level with the service information provided bythe user based on attributes of the user, such as but not limited to anexperience level of the user, an amount of experience that the user haswith the particular type of monitored asset, how often the user hascorrectly or incorrectly diagnosed issues related to this monitoredasset (or similar types of monitored assets) or correctly or incorrectlydiagnosed issues related to other monitored assets, or other factorsthat may be relevant to the accuracy of the service information providedby the user. In some implementations, the confidence level associatedwith the service information may be based at least in part on theconsistency between the diagnostic information and the repairinformation provided by the user. For example, the monitored asset 101may be a motor, and the server 106 may generate prognosis informationindicative of an issue with a bearing of the motor. The server 106 mayfurther generate more detailed prognosis information indicative of anissue with a bearing “inner race.” A user may assess the bearing andmake repairs. The diagnostic information provided by the user mayindicate that the bearing had a “bad inner race,” and the repairinformation may indicate that the “inner race was replaced.” The serviceinformation may be indicated to have a high confidence level in thissituation (which could be a quantitative value, an enumerated value or aqualitative value) because both the diagnostic information and therepair information were consistent with the prognosis information.Furthermore, where the service information is consistent with theprognosis information that was produced for the monitored asset 101, theprognosis information may be indicated to have a high confidence level.

Determining a confidence level and comparing the confidence level to athreshold may use a confidence level that is quantitative in nature. Forexample, a confidence level with a range between 1 and 10 may be used,with a value of 1 indicating low confidence and a value of 10 indicatinghigh confidence. A confidence level may, for example, be computed basedupon a combination of inputs, including one or more inputs of a userconfidence (e.g., user input following diagnosis and/or repair), a userexperience value and the equivalence between the prognosis informationand the service information. A confidence level may, for example, becomputed using a weighted, linear or non-linear combination of theinputs. A confidence level may be compared to a threshold value. Theserver 106 may be configured such that if a confidence level is at orabove a threshold, then server 106 tags the feature information used togenerate the prognosis information or feature information obtainedaround the time that the prognosis information was generated with alabel indicative of a condition of the monitored asset 101 that wasidentified in the prognosis information. The server 106 may also beconfigured to include the confidence level information with the taggedfeature information. Alternatively, the threshold may be set to 0, andthe server 106 may tag feature information and include the computedconfidence level in the tag or label. The tagged feature information maybe used to refine the training of the predictive algorithms. Tagging ofthe feature information is discussed in greater detail in the variousexample implementations that follow.

Alternatively, the confidence level and threshold may benon-quantitative in nature and may be represented by non-quantitativemechanisms. Examples of non-quantitative mechanisms include (a) thelogical match between prognosis and diagnostic information, (b) thelogical equivalence between prognosis, diagnostic information and repairinformation, or (c) the logical equivalence between prognosis,diagnostic information and repair information plus a second prognosis,following user repair of the asset which confirms the successful repairof the asset. In an example implementation, the confidence level may berepresented as an enumerated set of confidence values that includes“high,” “medium,” and “low” confidence levels. The high confidence levelmay be selected where the diagnostic information or the diagnosticinformation and the repair information correlate with the prognosisinformation, such as in the bearing example discussed above. The mediumconfidence level may be selected where the diagnostic information or therepair information indicate that there is a problem with the monitoredasset 101, but the prognosis information was not entirely correct. Forexample, referring back to the bearing example, assume that theprognosis information correctly identified that there was an issue withthe bearing, but in this additional example the exact prognosis does notmatch the actual condition indicated by the service information. In thisadditional example, the prognosis information indicates an inner raceproblem, but the diagnostic information and the repair informationindicate an outer race problem or misalignment. The confidence level inthe prognosis may be marked as “medium confidence” in these examples.The server 106 may use this confidence level information to indicatethat the predictive algorithms may be further refined with new trainingdata that may help distinguish between these situations. The newtraining data may be generated by associating a label with the featureinformation used to generate the prognosis information or featureinformation collected from a time period near when the prognosisinformation was generated. The label represents a condition of themonitored asset 101 that the predictive algorithms are trained torecognize. An indication may be included with the feature informationthat identifies whether or not the feature information is indicative ofthe condition of the monitored asset that was identified in theprognosis information. The inclusion of training data that is notindicative of a particular condition of the monitored asset may be usedto refine the training of the predictive algorithms and may reduce falsepositives. Finally, the low confidence level may be selected where thediagnostic information indicates that the monitored asset 101 appears tobe operating correctly. The server 106 may also use this confidencelevel information to indicate that the predictive algorithms may befurther refined with new training data that may help distinguish betweenthese situations. These examples are illustrative and do not limit thetechniques disclosed herein to these specific confidence levels.

In some implementations, the confidence level may be assumed. Forexample, the user or other technician may make repairs to the monitoredasset 101 in response to receiving the prognosis information withoutexpressly providing specific diagnostic information. The server 106 mayassume that the prognosis information was correct in this situation andmay assign a confidence level to the prognosis information. In someimplementations, the assumed confidence level in the prognosisinformation may be based on a confidence level associated with theservice information. Various combinations of quantitative, logical, andassumed confidence levels and thresholds may be used.

The server 106 may disregard or discount the diagnostic information, therepair information, or both provided by the user in response to theconfidence level associated with the prognosis information exceeding theconfidence level associated with the diagnostic information. Also, oralternatively, the server 106 may request that the status of themonitored asset 101 be reassessed and new service information providedin response to the confidence level associated with the prognosisinformation exceeding the confidence level associated with thediagnostic information. A different or more experienced user may be ableto provide a more reliable assessment the status of the asset. Theserver 106 may reassess the prognosis information and to refine thepredictive algorithms in response to multiple users providing diagnosticinformation that is contrary to the prognosis information.

Referring also to FIG. 2, the server 106 may include a monitoring unit202, a prediction/classification unit 203, a sensor data repository 204,a content provider 205, a work order management unit 206, a trainingdata repository 207, a learning unit 208, and a data tagging unit 209.The user device 107 may include a user application 210.

The monitoring unit 202 may send data to and receive data from thesensor(s) 102. The monitoring unit 202 may communicate with thesensor(s) 102 via the network 105 and the network device 104. Thesensor(s) 102 may send sensor data, feature information, or both to themonitoring unit 202. The monitoring unit 202 may store the sensor dataand feature information received from the sensor(s) 102 in the sensordata repository 204. The sensor data repository 204 may comprise adatabase, a cache, a filesystem, and/or other data storage means. Thecontents of the sensor data repository 204 may be stored for a shortperiod of time, or may be archived for long-term storage, and may beaccessed for processing the contents. In some implementations, thecontents of the sensor data repository 204 may be purged after thecontents are utilized to produce training data for the predictivealgorithms used by the server 106 to generate prognosis information. Themonitoring unit 202 may provide configuration information to thesensor(s) 102 from the prediction/classification unit 203.

The prediction/classification unit 203 may implement one or morepredictive algorithms that may be used to analyze feature informationassociated with a monitored asset 101 extracted from the sensor datacollected by the sensor(s) 102 associated with the monitored asset 101.The prediction/classification unit 203 may produce prognosis informationindicating an occurrence of a condition of the monitored asset. Thecondition may, for example, be a situation where the monitored asset isin need of repair, maintenance, or other attention. The featureinformation produced from the sensor data may, for example, indicatethat a characteristic associated with the monitored asset has fallenbelow or exceeded a threshold or has gone outside an expected range ofvalues for the characteristic. The feature information may includeinformation for more than one characteristic associated with themonitored asset. The prognosis information may, for example, indicate amalfunction of the monitored asset due to excessive vibrations beingdetected, a temperature associated with the monitored asset fallingoutside of an expected range, a loss of power, or a combination thereof.These examples illustrate some of the characteristics of a monitoredasset that may be monitored and do not limit the techniques disclosedherein to these specific characteristics or combination ofcharacteristics. The characteristics of a monitored asset may depend onthe type of asset being monitored and the types of sensors that arebeing used to monitor the monitored asset. For example, a pump may haveone or more sensors that monitor for vibrations and flow rate, while acomputing device may include a temperature sensor that monitors atemperature of the device and a humidity sensor to monitor ambienthumidity where the computing device is located.

The prediction/classification unit 203 may provide the prognosisinformation to the content provider 205. The content provider 205 mayprovide content to the user application 210 of the user device 107. Thecontent provider 205 may be a web server, and the user application 210may be a web browser application on the user device 107. The contentprovider 205 may push content, such as the prognosis information, to theuser application 210, and the user application 210 may display anindication to the user of the user device 107 that the prognosisinformation has been received and the content of the prognosisinformation. In some implementations, the prediction/classification unit203 may provide the prognosis information for a monitored asset to thework order management unit 206, and the work order management unit 206may produce a work order to have a technician or other human user checkthe status of the monitored asset. The work order management unit 206may send, to the user device 107, a work order or other request thatidentifies the monitored asset to be assessed by the technician or otheruser and includes the prognosis information that was produced by theprediction/classification unit 203 for the monitored asset. The userapplication 210 may display the work order and/or the prognosisinformation to the user. The user application 210 may also be configuredto provide information for locating the monitored asset. For example,the information for locating the monitored asset may include coordinatesof a location of the monitored asset, a photograph or diagram of themonitored asset or an example of the monitored asset, a map of afacility or geographic area in which the monitored asset is located, ora combination thereof. The user application 210 may display theprognosis information indicating an occurrence of a condition of themonitored asset. The user application 210 may display historicalinformation regarding the past performance of the monitored asset andinformation regarding past maintenance and repairs that have beenperformed on the monitored asset. The historical information may bestored in the diagnostic information repository 212, which may beupdated when a user provides diagnostic information in response to theprognosis information produced for a monitored asset, and in the repairand maintenance information repository 213, which may be updated whenmaintenance or repairs are performed on a monitored asset 101.

The user application 210 may provide a user interface in which the usermay enter service information regarding the monitored asset. The serviceinformation may include diagnostic information, repair information, orboth. The diagnostic information may include information indicative ofwhether the prognosis information produced by theprediction/classification unit 203 was correct. The repair informationmay include information indicative of repairs or maintenance performedon the monitored asset in response to the prognosis information. Therepairs or maintenance performed may be suggested by theprediction/classification unit 203, or the user responding to the workorder may determine which repairs or maintenance appear to be requiredby the monitored asset (if any). In some instances, the serviceinformation may include either diagnostic information or repairinformation. For example, in some instances a user may assess the statusof the monitored assets and provide diagnostic information indicatingwhether the prognosis information appears to be correct withoutperforming any repairs or maintenance on the monitored asset. In otherinstances, the user may perform repairs or maintenance on the monitoredasset and provide repair information indicative of the actions that weretaken on the monitored asset. In this latter example, the server 106 maytreat the repair information as a tacit agreement that the diagnosisinformation was correct due to the repairs or maintenance performed onthe monitored asset. The diagnostic information and the repairinformation may be structured to precisely indicate the currentcondition of the monitored asset 101 and exactly which repair ormaintenance actions were taken by the user. The user interface mayprovide a checklist or other structured such structured interface thatallows the user to select from list of options. The structured interfacemay present the user with a set of questions and a predetermined answerfor each question from which the user may select responses. In someimplementations, the user interface may include one or more unstructuredinputs in which the user may provide text feedback. In someimplementations, the service information may include images of themonitored asset to support the service information provided by the user.

The user interface of the user application 210 may provide an interfacein which the user may provide diagnostic information confirming whetherthe prognosis information produced for the monitored asset was correct.In some instances, the prediction/classification unit 203 may produceprognosis information indicating an occurrence of a condition of themonitored asset that is incorrect or appears to be incorrect to the userthat checks the status of the monitored asset. The user application 210allows the user to provide diagnostic information including details ofuser's perceived operating status of the monitored asset. The diagnosticinformation may include information that indicates why the user believesthat the prognosis information was incorrect in situations should theuser believe that the prognosis information was incorrect. Thediagnostic information entered via the user interface of the userapplication 210 may be provided to the data tagging unit 209, and thedata tagging unit 209 may tag sensor data, feature information, or both,associated with the monitored asset.

The data tagging unit 209 is configured to tag or label featureinformation to produce training data that may be used by the learningunit 208 to train the predictive algorithms used by theprediction/classification unit 203. The data tagging unit 209 may obtainsensor data and/or feature information extracted from the sensor datafrom the sensor data repository 204. The data tagging unit 209 may alsobe configured to receive service information for monitored assets 101that may include diagnostic information, repair information, or both.The work order management unit 206 may provide the service informationreceived from the user device 107 to the data tagging unit 209. The datatagging unit 209 may also be provided with the prognosis informationproduced by the prediction/classification unit 203.

The data tagging unit 209 may tag feature information by associating thefeature information with a label representing a possible condition ofthe monitored asset 101. The feature information included in the taggedfeature information may include feature information that was used toproduce the prognosis information. The feature information included inthe tagged feature information may also include feature information thatfalls within a predetermined time period of the prognosis informationbeing produced. The data tagging unit 209 is configured to tag thefeature information with a label indicative of a condition of themonitored asset 101 that is believed to be correct. The serviceinformation provides empirical evidence of the condition of themonitored asset 101 as directly observed by the user that provided theservice information. This empirical evidence, which may also be referredto as “ground truth,” may be used to prove or disprove the inference(s)made by the prediction algorithms to generate the prognosis information.The data tagging unit 209 may associate the feature information with anindication of whether the feature information is or is notrepresentative of a particular condition of the monitored asset 101. Thepredictive algorithms may be trained with this tagged featureinformation to refine the accuracy of the inferences that are made onuntagged feature information in the future and may improve the prognosisinformation generated by the predictive algorithms in the future.

The data tagging unit 209 may include asset information with the taggedfeature information. The asset information may include an asset type,asset parameters, sensor location(s) on the monitored asset 101, or acombination thereof. The asset type may provide context for thepredictive algorithms for determining whether tagged feature informationmay be useful for a particular monitored asset. Some example asset typesinclude but are not limited to “water pump” and “solar turbine.” Theasset type may be more specific and may include more specific details,such as a make or model number of the monitored asset 101. The assetparameters may include details regarding various aspects of themonitored asset 101 that may be useful for determining more refinedprognosis information for the monitored asset 101. Some examples of theasset parameters may include but are not limited to a motor speed of amotor of the monitored asset 101 and a number of blades of a turbine ofthe monitored asset 101. The sensor location information may indicatewhere on the monitored asset 101 each sensor 102 is located. Thelocation of the sensor 102 could impact the sensor data collected by thesensor 102 as well as the feature information derived from this sensordata. The sensor location information may be useful in determining whereto place sensors 102 on the same type or a similar type of monitoredasset 101 in the future. The example asset information provided hereinillustrates concepts disclosed herein and is not limiting.

The label and the feature information may be added to the training datarepository 207, which may be used by the learning unit 208 to refine thepredictive algorithms used by the prediction/classification unit 203.The feature information associated with the label may be obtained fromone or more of: (1) sensor data from sensors 102 associated with themonitored asset 101; (2) sensor data from sensors 102 associated with anenvironment in which the monitored asset 101 is operating (e.g., ambienttemperature sensors, audio sensors, optical sensors, vibration sensorsassociated with a floor, wall, or piece of equipment in which themonitored equipment); and (3) sensor data from equipment dependent onthe monitored asset 101 (e.g., downstream component experiences reducedflow rate from pump or itself overheats indicating that the pump (themonitored asset in this example) is experiencing a problem).

In some implementations, the sensor data repository 204 and the trainingdata repository 207 may be implemented as a single repository. In suchimplementations, the data tagging unit 209 may move the tagged featureinformation from one repository to another or may instead update therepository to include associate feature information to be tagged with alabel, and other information, such as but not limited to serviceinformation provided by the user and confidence level information forprognosis information, the service information, or both.

The learning unit 208 is configured to obtain the training data from thetraining data repository 207 to train the predictive algorithms used bythe prediction/classification unit 203. The tagged feature informationincluded in the training data includes labels for one or more possibleconditions associated with monitored assets and examples of featureinformation aligned in time with the occurrence of or imminentoccurrence of each of these conditions. The feature information includedin the tagged feature information may include feature information thatwas used to produce the prognosis information. The feature informationincluded in the tagged feature information may also include featureinformation that falls within a predetermined time period of theprognosis information being produced. As discussed above, the predictivealgorithms may have a classification aspect and a predictive aspect. Theclassification aspect may make a determination whether featureinformation obtained for a monitored asset is indicative of one of adiscrete number of possible conditions that the predictive algorithmshave been trained to identify as having occurred or the occurrence ofwhich is imminent. The predictive aspect may make a determination as toa likelihood of that an event related to the monitored asset 101. Theprognosis information generated for the monitored asset may includeinformation provided by the classification aspect, the predictiveaspect, or both. The classification aspect of the predictive algorithmsmay use a classification model that distinguishes between various statesor possible conditions in which a monitored asset may be operating(e.g., operating normally, specific component failure, etc.). Thepredictive algorithms may be used to analyze the untagged data usingregression, classification, neural networks, deep learning, forms ofmachine learning, or other techniques, or a combination thereof toproduce prognosis information for the monitored asset for which theuntagged feature information was obtained. The predictive algorithms mayuse various learning methods, such as but not limited to logisticregression, naïve Bayes classifier(s), support vector machines (SVMs),decision tree learning algorithms, boosted trees learning algorithms,random forest learning algorithms, neural network(s), nearest neighboralgorithms.

The learning unit 208 be configured to use the training data to modifythe classification variable(s), decision threshold(s), or both used bythe predictive algorithms of the prediction/classification unit 203. Thelearning unit 208 may send the modified classification variable(s),decision threshold(s), or both to the sensor(s) 102 associated with themonitored asset. The sensor(s) 102 may use this information to changethe operating parameters of the sensor(s) 102 and may cause the sensor102 to add, remove, or modify features to be extracted from the sensordata obtained by the sensor 102. For example, if the sensor 102 isconfigured to monitor vibration of the monitored asset 101, someclassification variables of interest may include but are not limited tothe Root Mean Square Amplitude (RMS) of the vibration, the crest factorof the vibration, the shape factor of the vibration, the mean point ofthe vibration, the skewness of the vibration, the kurtosis of thevibration, and/or other such aspects of the vibration that may becomputing in a time or frequency domain.

In an example implementation to illustrate these concepts, theprediction/classification unit 203 produces first prognosis informationfor the monitored asset 101 at a first time (t₁) which indicated thatthe monitored asset 101 appeared to be operating correctly, and theprediction/classification unit 203 produces second prognosis informationfor the monitored asset 101 at a second time (t₂). The second prognosisinformation is indicative of a condition of the monitored asset havingoccurred or is imminent. The data tagging unit 209 may obtain featureinformation stored in the sensor data repository 204 associated with themonitored asset for a period of time around the second time (t₂) (e.g.,a predetermined amount of time before t₂, after t₂, or both) in whichfeature information indicative of the occurrence of the condition waslikely to have been determined for the monitored asset 101. The datatagging unit 209 may associate the feature information with a labelbased on the service information that was obtained in response to theprognosis information. The service information may be used to confirmthe occurrence of a possible condition of the monitored asset and thefeature information may be associated with a label that represents thatcondition.

The data tagging unit 209 may also be configured to add confidence levelinformation to the tagged feature information. As discussed in thepreceding examples, the prediction/classification unit 203 may produce aconfidence level for the prognosis information, and the work ordermanagement unit 206 may produce a confidence level in the serviceinformation provided by the user. The data tagging unit 209 may includeone or both types of confidence information with the tagged featureinformation. The training of the predictive algorithms may use thisconfidence level information to further refine the predictive algorithmsand to produce improved prognosis information indicative of anoccurrence of a possible condition. For example, the training process ofthe predictive algorithms may assign a higher weight to tagged featureinformation having higher confidence levels than tagged featureinformation associated with lower or no confidence levels.

The data tagging unit 209 may manage different scenarios that may occurregarding the prognosis information and the service information,including but not limited to: (1) the service information indicates thatthe prognosis information was correct; (2) the service informationindicates that the prognosis information was correct, but the repairs ormaintenance performed on the monitored asset did not correct thecondition of the monitored asset; and (3) the service informationindicates that the prognosis information appears to be incorrect.

In the first scenario, the prognosis information may be provided to theuser device 107. The user may physically assess the status of themonitored asset 101 to determine whether the prognosis information iscorrect. The user can determine whether the condition indicated inprognosis information has occurred or appears to be about to occur. Theuser may provide diagnostic information indicative of whether theprognosis information was correct. The user may provide repairinformation indicative of repairs or maintenance performed on themonitored asset 101 in response to the prognosis information. In someinstances, the user does not provide diagnostic information but doesprovide repair information. In such instances, the data tagging unit 209may treat the repair information as a tacit indication that theprognosis information was correct. The data tagging unit 209 mayassociate the diagnostic information, repair information, or both withthe feature information. The association of feature information withinformation about the state of a monitored asset 101 (e.g. diagnosticinformation, repair information) thought to be true, is referred toherein as ‘tagging.’ The repair information may later be used to suggestrepairs or maintenance in response to the prediction/classification unit203 determining that the same condition has occurred with the samemonitored asset or with a monitored asset of a similar type as themonitored asset for which the repairs or maintenance were originallyperformed.

In the second scenario discussed above, the user assesses the operationof the monitored asset 101 and provides feedback to the server 106 inthe form of diagnostic information. In this scenario, the user performsrepairs or maintenance on the monitored asset 101. The user providesrepair information to the server 106 via the user application 210 of theuser device 107. The term “repair information” is used herein tocollectively refer to information associated with maintenance andrepairs that the user has performed on the monitored asset.

Once the user has completed the repairs or maintenance on the monitoredasset 101, the sensor(s) 102 associated with the monitored asset maycontinue to monitor the monitored asset 101 and to provide featureinformation to the server 106. The prediction/classification unit 203 ofthe server 106 can analyze the feature information and produce newprognosis information for the monitored asset 101. The data tagging unit209 can be configured to determine whether the condition of themonitored asset 101 has been corrected based on the prognosisinformation. If the prognosis information indicates that the conditionof the monitored asset 101 has not been corrected, the data tagging unit209 may associate the diagnosis information with the feature informationand may either (1) disregard or discount the repair information providedby the user when generating the tagged data, or (2) associate the repairinformation with the feature information and include an indicationindicative of the repairs or maintenance having been performed havingfailed to resolve the condition of the monitored asset 101. The server106 can use such an indication when providing suggested repairs ormaintenance to a user in response to this condition occurring in thefuture. The suggested repair information may include a description pastmaintenance or repairs that were performed on the monitored asset ormonitored assets of a similar type and whether these repairs ormaintenance successfully resolved the condition of the monitored asset101. This information may help a user determine what didn't work to fixa condition so that unnecessary repairs or maintenance can be avoided.

In the third scenario discussed above, the prognosis information appearsto be incorrect. The user assesses the status of the monitored asset101, and determines that the prognosis information appears to beincorrect. The user may send diagnostic information to the server 106via the user application 210 of the user device 107 that indicates thatthe prognosis information produced by the prediction/classification unit203 appears to be incorrect. The data tagging unit 209 may be configureddisregard or discount the prognosis information responsive to theprognosis information being incorrect. For example, the data taggingunit 209 may not tag any feature information associated with theprognosis information for inclusion in the training data repository 207.The data tagging unit 209 may, alternatively, override the serviceinformation provided by the user. The prediction/classification unit 203may produce a confidence level in the prognosis information and may alsobe configured to produce a confidence level in the service informationprovided by the user. The confidence level in the prognosis informationmay be based on historical data, stored for example in the diagnosticinformation repository 212, the repair and maintenance informationrepository 213, or both. The historical data may be used to indicatethat the diagnosis of the condition of the monitored asset was correctin the past based on the same or similar feature information beingassessed by the predictive algorithms used by theprediction/classification unit 203. The work order management unit 206may produce a confidence level in the service information provided bythe user in response to the prognosis information. The confidence levelassociated with the service information may be based on attributes ofthe user, such as but not limited to an experience level of the user, anamount of experience that the user has with the particular type ofmonitored asset, how often the user has correctly or incorrectly hasdiagnosed conditions related to this monitored asset or correctly orincorrectly diagnosed conditions related to other monitored assets, orother factors that may be relevant to the accuracy of the diagnosisprovided by the user. The data tagging unit 209 may reject or ignoreservice information provided by the user that contradicts the prognosisinformation where the confidence level for the prognosis exceeds theconfidence level for the service information provided by the user. Inthis scenario, the data tagging unit 209 may instruct the work ordermanagement unit 206 to request that the user reassess the status of themonitored asset 101 or that another user assess the status of themonitored asset 101. The data tagging unit 209 may instruct the workorder management unit 206 to request that the user or another userperform repairs or maintenance that have been successful in the past forresolving the condition of the monitored asset 101. The server 106 maythen monitor the status of the asset to determine whether the conditionis subsequently resolved in response to the repairs or maintenance.

Referring to FIG. 3, an example of one of the sensors 102 shown in FIGS.1 and 2 includes a network interface 301, a memory 302, a pre-processingunit 307, one or more sensors 308, a processor 309, and a user interface310. The memory 302 may also include one or more of sensor data 303,sensor configuration information 304, feature information 305, andprogram code 306.

The sensor(s) 308 may sense one or more characteristics associated witha monitored asset and to produce sensor data. In some implementations,the sensor data may be stored in the memory as sensor data 303 afterbeing output by the sensors. In some implementations, the pre-processingunit 307 may convert analog sensor data produced by the sensor(s) 308into digital sensor data. The pre-processing unit 307 may store thedigital sensor data as sensor data 303 in the memory 302. Thepre-processing unit 307 may perform other processing on the sensor data303 produced by the sensor(s) 308.

As shown, the sensor 102 may include a network interface 301 that mayprovide wired and/or wireless network connectivity to the sensor 102.The network interface 301 may include one or more local area networktransceivers, one or more wide area network transceiver(s), or both thatmay be connected to one or more antennas. The one or more local areanetwork transceivers comprise suitable devices, circuits, hardware,and/or software for communicating with and/or detecting signals to/froma wireless local area network (WLAN) wireless access point. In someimplementations, the network device 104 may comprise a WLAN wirelessaccess point, and the sensor 102 may use the WLAN transceivers to senddata to and receive data from the network device 104. The wide areanetwork transceiver(s) may comprise suitable devices, circuits,hardware, and/or software for communicating with and/or detectingsignals from one or more wireless wide area network (WWAN) wireless basestations. The sensor 102 may include one or more wired network interfacecomponents that may enable the sensor 102 to communicate with thenetwork 105 or the network device 104.

The sensor 102 may in some implementations comprise a user interface 310that may allow a user to configure the sensor 102. The user interface310 may comprise one or more buttons and/or a keypad for entering thesensor configuration information 304. The user interface 310 may includea display, such as liquid crystal display (LCD), a touchscreen, or othersuitable display that may be used to display sensor configurationoptions and/or status to a user. The user interface 310 may also beconfigured to facilitate a user interacting with a computing device,such as user device 107, which may display a user interface on atouchscreen or other display of the computing device and include akeypad or other suitable means for receiving user inputs for enteringthe sensor configuration information 304.

The processor 309 may be communicably coupled with the network interface301, the memory 302, the user interface 310, and other components of thesensor 102. The processor 309 may include one or more microprocessors,microcontrollers, and/or digital signal processors that provideprocessing functions, as well as other calculation and controlfunctionality. In some implementations, the processor 309 may alsoimplement the functionality of the pre-processing unit 307.

The processor 309 may be coupled to storage media (e.g., the memory 302)for storing data and software instructions for executing programmedfunctionality within the sensor 102. The memory 302 may be on-board theprocessor 309 (e.g., within the same integrated circuit (IC) package),and/or the memory may be external memory to the processor andfunctionally coupled over a data bus.

The feature extraction unit 311 of the sensor 102 may extract featuresfrom the sensor data to produce extracted feature information, and mayselect at least a subset of the extracted feature information using thefeature extraction unit 311 to produce active feature information. Thefeature extraction unit 311 may perform feature extraction in which thesensor data obtained by the sensor(s) 308 is processed to analyze thesensor data to produce information that may be used by the predictivealgorithms to discriminate between possible conditions of a monitoredasset. The values of various features or combinations thereof may beindicative of the different possible conditions for which the predictivealgorithms have been trained. The predictive algorithms used by theprediction/classification unit 203 of the server 106 may be trained bythe learning unit 208 using feature information that has been “tagged”or labeled to indicate that a certain possible condition of themonitored asset 101 is present. The tagged data may include values forone or more of the features indicative of the occurrence of thatcondition. Once the algorithms have been trained by the learning unit208, the predictive algorithms may be provided with untagged data. Thepredictive algorithms may be used to analyze the untagged data usingregression, classification, other techniques, or a combination thereofto produce prognosis information for the monitored asset for which theuntagged feature information was obtained. The prognosis information mayindicate that a possible condition has occurred based on the analysis ofthe untagged feature information. Whether this prognosis information iscorrect may be verified by a technician or other user that may assessthe condition of the monitored asset.

The feature extraction unit 311 may use various feature extractiontechniques for extracting the feature information from the sensor data.The techniques disclosed herein do not require a specific featureextraction technique. The feature extraction unit 311 may be implementedin hardware instead of software or as a combination of hardware andsoftware. The feature extraction unit 311 may be implemented one or moreapplication specific integrated circuits (ASICs), programmable logicdevices (PLDs), field programmable gate arrays (FPGAs), or otherelectronic units designed to perform the functions described herein, ora combination thereof. The feature extraction unit 311 may beimplemented, at least in part, by processor executable program code.

The feature extraction unit 311 may determine a dynamic baseline valuefor each of the features that the feature extraction unit 311 extractsfrom the sensor data. The dynamic baseline value represents an expectedvalue for each of the one or more features over time. The featureextraction unit 311 may develop the dynamic baseline over time based onobserved sensor data for a monitored asset 101. In some implementations,the feature extraction unit 311 may be provided with default dynamicbaseline information that may be used to provide an initial expectedbaseline values before the sensor 102 has had time to observe asufficient amount of information to produce a dynamic baseline for amonitored asset. The default dynamic baseline information may beprovided by the server 106, and may be based on attributes of themonitored asset, attributes of a plurality of similar monitored assets,expected environmental conditions at the location where the monitoredasset is located, other factors that may influence one or morecharacteristic(s) of the monitored asset that may be monitored by thesensor(s) 308. These other factors may include changes in thecharacteristic(s) of the monitored asset 101 that are monitored by thesensor(s) 308 that occur over time due to normal wear and tearexperienced by the monitored asset 101. The dynamic baseline determinedby the feature extraction unit 311 for this feature may take intoaccount these changes in the expected value of the feature extractedfrom the sensor data over time. The server 106 may provide informationas to expected changes in the patterns of these characteristic(s) overtime that have been generated by observing the operation of the same orsimilar type of monitored asset over time. The feature extraction unit311 may use this information when determining the dynamic baseline forthe monitored asset.

The feature extraction unit 311 may obtain sensor configurationinformation from the server 106 via the network interface 301. Thesensor configuration information may include information forconfiguration various aspects of the operation of the sensor 102, suchas but not limited to changing the set of feature information that thefeature extraction unit 311 extracts from the sensor data, changing theset of active features for which the feature extraction unit 311 sendsactive feature information to the server 106, the rate at which sensordata is collected, the rate at which feature extraction is performed onthe sensor data by the feature extraction unit 311, a rate at which thefeature information is reported to the server 106 by the featureextraction unit 311.

In an illustrative implementation, the monitored asset 101 is a pump,and the sensor 102 includes sensor(s) 308 to collect the followingsensor data: (1) flow rate from the pump and (2) vibration data from atleast one point on the housing of the pump. The feature extraction unit311 may analyze the sensor data to generate metrics that may beindicative of certain possible conditions that may occur with the pump.In this example, the feature extraction unit 311 may extract flow ratefeatures, vibration data features, or a combination thereof from thesensor data that diverge from an expected baseline value, and reportthis information to the server 106 in active feature information. Thedynamic baseline for the pump may include expected vibration patternsand flow rates for the pump that have been developed over time.

The server 106 receives the feature information from the sensor 102, andthe prognosis/classification unit 203 of the server 106 may analyze thisinformation using one or more predictive algorithms to determine whethera possible condition with the pump has occurred. For example, thepredictive algorithms may have been trained for a “damaged impeller”condition which is characterized by vibration of the pump exceeding apredetermined threshold and a flow rate of the pump decreasing below apredetermined threshold. In response to the predictive algorithmsgenerating prognosis information that indicates that the damagedimpeller condition has occurred, a technician or other may be dispatchedto assess the condition of the pump and may provide service informationto the server 106 that includes diagnostic information, repairinformation, or both. In a situation where the service informationsupports the occurrence of the damaged impeller condition, the user mayhave noted that the impeller was suffering from an imbalance or fromerosion. Alternatively, the service information may not support theoccurrence of the damaged impeller condition and may instead indicatethat the pump was suffering from bent shaft. In this scenario, theservice information confirms that the pump was operating in a degradedstate. The feature information collected around the time that theprognosis information was generated may be tagged with a first labelindicative of the pump operating in a degraded state. The same set offeature information may also be tagged with a second label thatindicates the specific condition that the pump experience (e.g. bentshaft or the damaged impeller). The predictive algorithms may be trainedwith the information for each of these scenarios in an attempt toidentify feature information that may be used to discriminate betweenthe occurrence of these two possible conditions. For example, avibration pattern indicative of impeller damage may be different than avibration pattern indicative of a bent shaft. The learning unit 208 mayidentify such a difference in the feature information obtained from thesensor(s) 102 associated with the pump, and to train the predictivealgorithms so that the predictive algorithms may distinguish betweenthese two conditions. The preceding example illustrates the conceptsherein and does not limit the techniques disclosed herein thisparticular type of monitored asset or scenario.

Referring to FIG. 4 is an example of the server 106 show in FIGS. 1 and2. While FIG. 4 illustrates the server 106 as being implemented as asingle device, the functionality of the server 106 may alternatively beimplemented in multiple physical devices, virtual devices or somecombination thereof. An example of the server 106 includes a processor401, a user interface 402, a network interface 403, a memory 404. Theserver 106 may also include a monitoring unit 202, aprediction/classification unit 203, a content provider 205, a work ordermanagement unit 206, a data tagging unit 209, and a sensor configurationunit 409. The memory 404 may include the sensor data repository 204,training data repository 207, work order information repository 211,diagnostic information repository 212, and repair and maintenanceinformation repository 213. The sensor data repository 204, trainingdata repository 207, work order information repository 211, diagnosticinformation repository 212, and the repair and maintenance informationrepository 213 may comprise information used by the various component ofthe server 106 as described in the preceding examples.

The processor 401 may be communicably coupled with the user interface402, the network interface 403, the memory 404, the monitoring unit 202,the prediction/classification unit 203, the content provider 205, thework order management unit 206, the data tagging unit 209, and thesensor configuration unit 409. The processor 401 may include one or moremicroprocessors, microcontrollers, and/or digital signal processors thatprovide processing functions, as well as other calculation and controlfunctionality. The processor 401 may be coupled to storage media (e.g.,the memory 404) for storing data and software instructions for executingprogrammed functionality within the server 106. The memory 404 may beon-board the processor 401 (e.g., within the same IC package), and/orthe memory may be external memory to the processor and functionallycoupled over a data bus. Furthermore, the memory 404 may comprisevolatile memory, non-volatile memory, or a combination thereof. Thememory 404 may include program code 405, which comprisesprocessor-executable program code that may be executed by the processor401.

The network interface 403 that may provide wired and/or wireless networkconnectivity to the server 106. The network interface 403 may includeone or more local area network transceivers, one or more wide areanetwork transceiver(s), or both that may be connected to one or moreantennas. The one or more local area network transceivers comprisesuitable devices, circuits, hardware, and/or software for communicatingwith and/or detecting signals to/from a wireless local area network(WLAN) wireless access point associated with the network 105 or withanother network communicably coupled to the network 105. The wide areanetwork transceiver(s) may comprise suitable devices, circuits,hardware, and/or software for communicating with and/or detectingsignals from one or more wireless wide area network (WWAN) wireless basestations associated with the network 105 or with another networkcommunicably coupled to the network 105. The server 106 may communicatewith the sensor(s) 102 and the user device 107 via the network 105. Theserver 106 may include one or more wired network interface componentsthat may enable the user device 107 to communicate with the server 106and/or another networked device.

The user interface 402 may provide suitable interface systems foroutputting audio and/visual content, and for facilitating userinteraction with the server 106. For example, the user interface 402 maycomprise one or more of a microphone and/or a speaker for outputtingaudio content and for receiving audio input, a keypad, a keyboard,and/or a touchscreen for receiving user inputs, and a display (which maybe separate from the touchscreen or be the touchscreen) for displayingvisual content.

The monitoring unit 202, the prediction/classification unit 203, thecontent provider 205, the work order management unit 206, and the datatagging unit 209 operate as discussed in the preceding examples.Furthermore, these units and the sensor configuration unit 409 may beimplemented as processor-executable program code, hardware, or acombination thereof. The sensor configuration unit 409 may send sensorconfiguration information to the sensor(s) 102. The sensor configurationinformation may include information for configuration various aspects ofthe operation of the sensor 102, such as but not limited to changing theset of feature information that the sensor 102 extracts from the sensordata, changing the set of active features for which the sensor 102 sendsactive feature information to the server 106, the rate at which featureextraction is performed on the sensor data by the sensor 102, and a rateat which the feature information is reported to the server 106 by thesensor 102.

Referring to FIG. 5, an example user device 107 shown in FIG. 2 includesa processor 501, a user interface 502, a network interface 503, a workorder processing unit 504, and a memory 505. The memory 505 may storework order information 506, diagnostic information 507, repairinformation 508, and executable program code 509. The user device 107may be a smartphone, a tablet computing device, laptop, or otherportable computing device. The user device 107 may also be stationary orsubstantially stationary, such as a desktop computing device.

The processor 501 may be communicably coupled with the user interface502, the network interface 503, the work order processing unit 504, andthe memory 505. The processor 501 may include one or moremicroprocessors, microcontrollers, and/or digital signal processors thatprovide processing functions, as well as other calculation and controlfunctionality. The processor 501 may be coupled to storage media (e.g.,the memory 505) for storing data and software instructions for executingprogrammed functionality within the user device 107. The memory 505 maybe on-board the processor 501 (e.g., within the same IC package), and/orthe memory may be external memory to the processor and functionallycoupled over a data bus. Furthermore, the memory 505 may comprisevolatile memory, non-volatile memory, or a combination thereof.

The user device 107 may include a network interface 503 that may providewired and/or wireless network connectivity to the user device 107. Thenetwork interface 503 may include one or more local area networktransceivers, one or more wide area network transceiver(s), or both thatmay be connected to one or more antennas. The one or more local areanetwork transceivers comprise suitable devices, circuits, hardware,and/or software for communicating with and/or detecting signals to/froma wireless local area network (WLAN) wireless access point associatedwith the network 105 or with another network device communicably coupledto the network 105. The wide area network transceiver(s) may comprisesuitable devices, circuits, hardware, and/or software for communicatingwith and/or detecting signals from one or more wireless wide areanetwork (WWAN) wireless base stations associated with the network 105 orwith another network communicably coupled to the network 105. The userdevice 107 may communicate with the server 106 via the network 105. Theuser device 107 may include one or more wired network interfacecomponents that may enable the user device 107 to communicate with theserver 106 and/or another networked device.

The user interface 502 provides suitable interface systems foroutputting audio and/visual content, and for facilitating userinteraction with the user device 107. For example, the user interface502 may include one or more of a microphone and/or a speaker foroutputting audio content and for receiving audio input, a keypad and/ora touchscreen for receiving user inputs, and a display (which may beseparate from the touchscreen or be the touchscreen) for di splayingvisual content.

The work order processing unit 504 may implement the user application210 discussed with respect to FIG. 2. The work order processing unit 504may be implemented in processor-executable program code (e.g.,executable program code 509), hardware, or a combination thereof. Thework order processing unit 504 may receive a work order from the server106. The work order may include prognosis information for a monitoredasset 101 and other information, such as location information for themonitored asset that may assist the user in locating the monitoredasset. The work order processing unit 504 may provide a user interfacefor displaying the work order information, prognosis information, andlocation information for the monitored asset 101. The user interface mayalso be configured to display repair and maintenance history informationfor the monitored asset (where available), and the user interface mayalso include recommended repairs or maintenance for the monitored assetbased on the repair and maintenance history of the monitored asset, therepair and maintenance history for monitored assets of a similar type,or both. The user interface may also allow the user to enter serviceinformation, which may include diagnostic information 507, repairinformation 508, or both as discussed in the preceding examples. Thework order processing unit 504 may send the service information to theserver 106 using the network interface 503.

FIG. 6 is a flow diagram of an example process for monitoring amonitored asset and for generating a prognosis for the monitored assetaccording to the disclosure. The process illustrated in FIG. 6 may beperformed by the server 106 of the monitoring system illustrated in thepreceding figures. The process illustrated in FIG. 6 is an example onlyand is not limiting. The example process may be altered, e.g. by havingstages added, removed, rearranged, combined, performed concurrently, byhaving single stages split into multiple stages, or a combinationthereof.

The server 106 may determine prognosis information a monitored asset 101(stage 601). The prediction/classification unit 203 of the server 106may receive feature information from one or more sensor(s) 102associated with the monitored asset 101. The prediction/classificationunit 203 may produce the prognosis information by applying one or morepredictive algorithms to the feature information. The predictivealgorithms may be trained using tagged feature information. Taggedfeature information may include a label associated with a possiblecondition that had occurred with a monitored asset 101 and featureinformation based upon sensor data collected at the time of theoccurrence of that condition. The predictive algorithms may be trainedwith tagged feature information for multiple conditions. Once thepredictive algorithms have been trained, untagged feature informationcollected from the sensor(s) 102 associated with the monitored asset 101may then be provided to the prediction/classification unit 203 in orderto generate prognosis information for the monitored asset 101. Thepredictive algorithms may be trained using tagged feature information,such as training data in the training data repository 207, prior to thedeployment of the sensor on the monitored asset. Additionally, thepredictive algorithm may be trained using tagged feature informationincluding updates to the tagged feature information determined followingthe deployment the monitoring system or deployment of the sensor on themonitored asset.

The prediction/classification unit 203 may output the prognosisinformation, and the content provider 205 and/or the work ordermanagement unit 206 may provide the prognosis information to the userdevice 107. The monitoring system may be associated with a plurality ofuser devices, and each device may be associated with a user that themonitoring system may request make an assessment whether the prognosisinformation for the monitored asset is correct. The work ordermanagement unit 206 may select a user from the plurality of users basedon user availability, proximity to the monitored asset, user experiencewith the monitored asset or similar types of assets, other factors thatmay be used to select a user from the plurality of users, or combinationthereof. The work order management unit 206 may send a work order to theuser via the user device 107 that includes at least the prognosisinformation and information identifying the monitored asset. Otherinformation, such as map of the environment in which the monitoredlocation is located, proposed repairs or maintenance information, andhistorical information for the monitored asset may also be provided tothe user device 107 in addition to the work order.

The prediction/classification unit 203 may output the prognosisinformation, and the content provider 205 and/or the work ordermanagement unit 206 may provide the prognosis information to the userdevice 107. The monitoring system may be associated with a plurality ofuser devices, and each device may be associated with a user that themonitoring system may request make an assessment whether the prognosisinformation for the monitored asset is correct. The work ordermanagement unit 206 may select a user from the plurality of users basedon user availability, proximity to the monitored asset, user experiencewith the monitored asset or similar types of assets, other factors thatmay be used to select a user from the plurality of users, or combinationthereof. The work order management unit 206 may send a work order to theuser via the user device 107 that includes at least the prognosisinformation and information identifying the monitored asset. Otherinformation, such as map of the environment in which the monitoredlocation is located, proposed repairs or maintenance information, andhistorical information for the monitored asset may also be provided tothe user device 107 in addition to the work order.

Service information may be received from a user in response to theprognosis information (stage 602). The service information may includediagnostic information, repair information, or both. The diagnosticinformation includes information indicative of whether the prognosisinformation was correct, and the repair information includes informationindicative of repairs or maintenance performed on the monitored asset inresponse to the prognosis information. The user may provide the serviceinformation via the user application 210 of the user device 107.

A determination may be made whether the service information confirms theprognosis information (stage 603). If the service information indicatesthat the prognosis information was incorrect, the process may return tostage 601 where new prognosis information may be determined for themonitored asset. In some implementations, the prediction/classificationunit 203 may produce a confidence level for the prognosis information.The confidence level may be used to determine whether to disregard ordiscount diagnostic information provided by the user that asserts thatthe prognosis information is incorrect. In some implementations, if theconfidence level in the prognosis exceeds a confidence level threshold,the service information obtained from the user may be disregarded ordiscounted and the process may continue with stage 604. The data taggingunit 209 may not include the service information provided by the userwith the tagged feature information in this situation. In otherimplementations, both the prognosis information and the diagnosticinformation may be associated with a respective confidence level. If theconfidence level associated with the prognosis information is higherthan the confidence level associated with the diagnostic information,the process may continue with stage 604. Otherwise, the process mayreturn to stage 601 where new prognosis information may be determined.

Where the prognosis information is determined to be incorrect, and theprocess returns to stage 601, the server 106 may generate tagged featureinformation that may be used to train the predictive algorithms used togenerate the prognosis information. The data tagging unit 209 may tagthe feature information associated with the prognosis information toinclude an indication that the feature information is not indicative ofthe possible condition of the monitored asset 101 identified in theprognosis information. Such feature information may be used to train thepredictive algorithms as to feature information that is not indicativeof the occurrence of a particular possible condition and may be used torefine the predictive algorithms.

The server 106 may make a determination whether a user made repairs ormaintenance on the monitored asset 101 (stage 604). If the user maderepairs or performed maintenance on the monitored asset 101, the processmay continue with stage 606. As discussed in the preceding examples, theuser may provide service information, which may include diagnosticinformation indicative of whether the prognosis information was correct,and repair information that indicates whether the user performed repairsor maintenance on the monitored asset.

If the user made no repairs or performed no maintenance on the monitoredasset 101, the process may continue to stage 605, wherein the server 106may request service on the monitored asset. The work order managementunit 206 may produce a work order for a user to assess the monitoredasset again and to perform maintenance or repairs on the asset since theprognosis information produced by the server 106 was confirmed to becorrect in stage 603. However, no action was taken by the user at thattime to resolve the condition of the monitored asset 101.

A determination may be made whether the condition of the monitored assethas been resolved (stage 606). The user that responded to the request toperform service on the monitored asset 101 in stage 605 may haveprovided repair information to the server 106 via the user application210 of the user device 107. The monitoring unit 202 of the server 106may collect additional sensor data and/or feature information from thesensor(s) 102 associated with the monitored asset 101. The monitoringunit 202 may extract feature information from the sensor data inresponse to additional sensor data being obtained from the sensor(s) 102associated with the monitored asset. The prediction/classification unit203 may analyze the additional feature information to produce newprognosis information for the monitored asset 101.

If the new prognosis information is indicative of the condition of themonitored asset 101 having been resolved, the process may return tostage 601. Before returning to stage 601, the data tagging unit 209 ofthe server 106 may tag the feature information as discussed in thepreceding examples. The data tagging unit 209 may include serviceinformation provided by the user with the tagged feature information.The data tagging unit 209 may also include confidence level informationfor the prognosis information, the service information, or both in thetagged feature information.

Otherwise if the new prognosis information is not indicative of thecondition of the monitored asset having been resolved, the process mayreturn to stage 605 where a request for service on the monitored asset101 may be made. The user may assess the current state of the monitoredasset 101 and determine whether additional repairs or maintenance on themonitored asset 101 may be necessary. The data tagging unit 209 may tagthe feature information associated with the prognosis information toinclude an indication that the feature information is not indicative ofthe possible condition of the monitored asset 101 identified in theprognosis information. Such feature information may be used to train thepredictive algorithms as to feature information that is not indicativeof the occurrence of a particular possible condition and may be used torefine the predictive algorithms.

FIG. 7 is a flow diagram of an example process for monitoring amonitored asset and for generating a prognosis for the monitored assetaccording to the disclosure. The server 106 provides the means forimplementing the process illustrated in FIG. 7. The process illustratedin FIG. 7 is an example only and is not limiting. The example processmay be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof.

Feature information associated with sensor data associated with amonitored asset may be obtained (stage 701). The monitoring unit 202 ofthe server 106 may obtain feature information from one more sensors 102configured to sense one or more characteristics associated with amonitored asset to produce sensor data, and to extract featureinformation from the sensor data. The monitoring unit 202 may providethe feature information obtained from the sensor(s) to theprediction/classification unit 203 of the server 106.

The feature information may be analyzed using a predictive algorithm toproduce prognosis information (stage 702). The prediction/classificationunit 203 may analyze the feature information using one or morepredictive algorithms to produce prognosis information for the monitoredasset. The prognosis information is indicative of an occurrence of acondition of the monitored asset. The predictive algorithms may betrained using tagged data that associates a label for a particularpossible condition with feature information that is indicative of anoccurrence of that condition. The types of conditions that may beincluded in the training data, and the types of features associated witheach condition dependent on the type of monitored assets for which thepredictive algorithms have been trained.

Service information may be received from a user in response to theprognosis information (stage 703). As discussed in the precedingexamples, the prognosis information produced by the server 106 may beprovided to a user who may assess the status of the monitored asset andmake a determination whether the prognosis information was correct. Theprognosis information may be sent to the user device 107, and the userapplication 210 may present to the prognosis information, historicalservice information associated with the monitored asset, proposedrepairs or maintenance. The user may provide service information via theuser application 210 that may include diagnostic information, repairinformation, or both.

A confidence level may be determined for the service information (stage704). The work order management unit 206 may produce a confidence levelin the service information provided by the user in response to theprognosis information. The confidence level associated with the serviceinformation may be based on attributes of the user, such as but notlimited to an experience level of the user, an amount of experience thatthe user has with the particular type of monitored asset, how often theuser has correctly or incorrectly has diagnosed conditions related tothis monitored asset or correctly or incorrectly diagnosed conditionsrelated to other monitored assets, or other factors that may be relevantto the accuracy of the diagnosis provided by the user.

The feature information may be tagged with the service information basedat least in part on the confidence level (stage 705). As discussed inthe preceding examples, data tagging unit 209 may include the serviceinformation in the tagged feature information, unless the confidencelevel indicates that the service information is too unreliable to beutilized. The data tagging unit 209 may disregard or discount serviceinformation provided by the user that contradicts the prognosisinformation where the confidence level for the prognosis exceeds theconfidence level for the service information provided by the user. Thedata tagging unit 209 may disregard or discount the service informationif the confidence level for the service information falls below apredetermined threshold. The data tagging unit 209 may disregard ordiscount the service information by not including the serviceinformation in the tagged feature information. The data tagging unit 209may include the confidence level information in the tagged featureinformation along with the service data.

The predictive algorithm may be trained based on tagged featureinformation (stage 706). The data tagging unit 209 of the server 106 maytag the feature information used to produce the prognosis information inresponse to the service information indicating that the prognosisinformation was correct. The tagged feature information may be used torefine the training of the prediction algorithms used by theprediction/classification unit 203 of the server 106. An example processfor tagging data is illustrated in FIG. 8.

FIG. 8 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.7. The server 106 provides the means for implementing the additionalstage illustrated in FIG. 8. The example illustrated in FIG. 8 is anexample only and is not limiting.

The feature information used to produce the prognosis information may betagged with a label associated with a possible condition, in response toand based upon the service information indicating that the prognosisinformation was correct (stage 801). The data tagging unit 209 may tagor label data to produce training data that may be used by the learningunit 208 to train the predictive algorithms used by theprediction/classification unit 203. The data tagging unit 209 may obtainfeature information extracted from the sensor data from the sensor datarepository 204. The data tagging unit 209 may obtain feature informationstored in the sensor data repository 204 associated with the monitoredasset for a period of time around which feature information indicativeof the occurrence of the condition were likely to have been determinedfor the monitored asset 101, as discussed above. The data tagging unit209 may also receive service information for monitored assets that mayinclude diagnostic information, repair information, or both. Thelearning unit 208 may obtain the tagged feature information from thetraining data repository 207 and train the predictive algorithms used bythe prediction/classification unit 203. The tagged feature informationmay be used to refine the predictive algorithms as discussed in thepreceding examples.

FIG. 9 is a flow diagram of an example process for determining conditionresolution information according to the disclosure. The processillustrated in FIG. 9 may be used to implement additional stages of theprocess illustrated in FIG. 7. The server 106 provides the means forimplementing the process illustrated in FIG. 9. The process illustratedin FIG. 9 is an example only and is not limiting. The example processmay be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof.

Additional sensor data associated with the monitored asset may beobtained (stage 901). The monitoring unit 202 of the server 106 mayobtain additional feature information from one more sensor 102configured to sense one or more characteristics associated with themonitored asset to produce additional sensor data, and to extract theadditional feature information from the additional sensor data. Themonitoring unit 202 may provide the additional feature informationobtained from the sensor(s) to the prediction/classification unit 203 ofthe server 106.

Condition resolution information may be determined (stage 902). Thecondition resolution information may indicate whether repairs ormaintenance performed on the monitored asset resolved the condition ofthe monitored asset. The prediction/classification unit 203 may produceadditional prognosis information for the monitored asset by analyzingthe additional feature information produced in stage 901. Theprediction/classification unit 203 may determine whether the prognosisinformation produced based on the additional feature informationindicates that the condition of the monitored asset 101 has beenresolved. If the condition has not been resolved, theprediction/classification unit 203 may provide the prognosis informationto the work order management unit 206. The work order management unit206 may select a technician or other user to assess the status of themonitored asset 101, and to make repairs or maintain the monitored asset101. If the condition has been resolved, the data tagging unit 209 maytag the feature information associated in time with the original orprior prognosis information to indicate that the original or priorprognosis information was correct and that repairs or maintenance thatwere performed on the monitored asset 101 corrected the condition of themonitored asset 101.

FIG. 10 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.7. The server 106 provides the means for implementing the exampleillustrated in FIG. 10. The example illustrated in FIG. 10 is an exampleonly and is not limiting.

Second prognosis information may be produced for a second monitoredasset using a second predictive algorithm (stage 1001). The secondpredictive algorithm may use the training data associated with the firstmonitored asset. The second monitored asset may be a same or similartype of assets as the first monitored asset. Theprediction/classification unit 203 of the server 106 may be trainedusing tagged data from a first monitored asset to produce prognosisinformation for a second monitored asset of a same or similar type asthe first monitored asset. The predictive algorithms may be trainedusing tagged data from a plurality of assets. These assets may be of asame type (e.g., a specific make and model of pump) or a similar type(e.g. a similar class of pump). Training the predictive algorithms witha diverse data set may help to refine the predictive algorithms toprovide better prognosis results. Similarly, the predictive algorithmsmay be trained with data obtained from a single monitored asset 101 maybe used to train the predictive algorithms to analyze featureinformation and produce prognosis information for other similarmonitored assets 101. A single predictive algorithm may be trained usingtraining data associated with more than one monitored asset.Alternatively, more than one predictive algorithm may be trained. Eachpredictive algorithm may use the training data associated with one ormore of the assets being monitored.

FIG. 11 is a flow diagram of an example process for training apredictive algorithm with feature information tagged with repairinformation according to the disclosure. The process illustrated in FIG.11 may be used to implement additional stages of the process illustratedin FIG. 9. The server 106 provides the means for implementing theprocess illustrated in FIG. 11. The process illustrated in FIG. 11 is anexample only and is not limiting. The example process may be altered,e.g. by having stages added, removed, rearranged, combined, performedconcurrently, by having single stages split into multiple stages, or acombination thereof.

The tagged feature information may be updated with the repairinformation (stage 1101). The service information provided by the uservia the user application 210 of the user device may include repairinformation indicative of repairs or maintenance that the user performedon the monitored asset. In the process illustrated in FIG. 11, thecondition resolution information indicates that the repairs ormaintenance performed on the monitored asset 101 by the user appear tohave successfully resolved the condition of the monitored asset 101. Thefeature information, associated in time with the prior prognosisinformation, may be tagged with the repair information and an indicationthat the repairs successfully addressed the condition of the monitoredasset 101. The data tagging unit 209 may store the tagged featureinformation in the training data repository 207.

The predictive algorithms may be trained based at least in part on thetagged feature information (stage 1102). The learning unit 208 mayobtain the tagged feature information from the training data repository207 and train the predictive algorithms used by theprediction/classification unit 203. The tagged feature information maybe used to refine the predictive algorithms.

FIG. 12 is a flow diagram of an example process for training apredictive algorithm with feature information tagged with repairinformation according to the disclosure. The process illustrated in FIG.12 may be used to implement additional stages of the process illustratedin FIG. 9. The server 106 provides the means for implementing theprocess illustrated in FIG. 12. The process illustrated in FIG. 12 is anexample only and is not limiting. The example process may be altered,e.g. by having stages added, removed, rearranged, combined, performedconcurrently, by having single stages split into multiple stages, or acombination thereof.

The tagged feature information may be updated with the repairinformation and an indication that the repairs or maintenance performedon the monitored asset has not resolved the condition (stage 1201). Thedata tagging unit 209 may add the repair information to the taggedfeature information that is added to the training data repository 207.In the process illustrated in FIG. 12, the condition resolutioninformation indicates that the repairs or maintenance performed on themonitored asset 101 by the user appear not to have resolved thecondition of the monitored asset 101. The tagged feature information maybe updated to include the repair information and an indication that therepairs failed to address the condition of the monitored asset 101. Thedata tagging unit 209 may store the tagged feature information in thetraining data repository 207.

The predictive algorithms may be trained based as least in part on thetagged feature information (stage 1202). The learning unit 208 mayobtain the tagged feature information from the training data repository207 and train the predictive algorithms used by theprediction/classification unit 203. The tagged feature information maybe used to refine the predictive algorithms.

FIG. 13 is a flow diagram of an example process for obtaining serviceinformation for a monitored asset according to the disclosure. Theprocess illustrated in FIG. 13 may be used to implement additionalstages of the process illustrated in FIG. 7. The server 106 provides themeans for implementing the process illustrated in FIG. 13. The processillustrated in FIG. 13 is an example only and is not limiting. Theexample process may be altered, e.g. by having stages added, removed,rearranged, combined, performed concurrently, by having single stagessplit into multiple stages, or a combination thereof.

The prognosis information may be sent to a mobile device of a user(stage 1301). As discussed in the preceding examples, theprediction/classification unit 203 may provide the prognosis informationthe content provider 205 and/or the work order management unit 206. Theprognosis information may be displayed to a user of the user device 107.The prognosis information may be displayed with a work order requestingthat the user assess the status of the monitored asset 101. The user mayalso be provided with additional information, such as information toassist in locating the monitored asset 101. Historical information forthe monitored asset 101 may also be provided that includes pastmaintenance and repairs that have been performed on the monitored asset101. Information that includes proposed repairs or maintenance may alsobe included with the prognosis information provided to the user.

Service information may be received in response to sending the prognosisinformation (stage 1302). The user may provide service information tothe server 106 via the user device 107. The service information mayinclude diagnostic information, repair information, or both. Thediagnostic information may include information indicative of whether theprognosis information produced by the server 106 for the monitored assetwas correct. The repair information may comprise information indicativeof repairs or maintenance performed on the monitored asset in responseto the prognosis information.

FIG. 14 is a flow diagram of an example process for associating aconfidence level with the prognosis information according to thedisclosure. The process illustrated in FIG. 14 may be used to implementadditional stages of the process illustrated in FIG. 7. The server 106provides the means for implementing the process illustrated in FIG. 14.The process illustrated in FIG. 14 is an example only and is notlimiting. The example process may be altered, e.g. by having stagesadded, removed, rearranged, combined, performed concurrently, by havingsingle stages split into multiple stages, or a combination thereof.

A confidence level associated with the prognosis information may bedetermined (stage 1401). As discussed in the preceding examples, theprediction/classification unit 203 may produce a confidence level forthe prognosis information and a confidence level for the serviceinformation provided by the user. The confidence level in the prognosisinformation may be based on historical data, stored for example in thediagnostic information repository 212, the repair and maintenanceinformation repository 213, or both, which may be used to indicate thatthe diagnosis of the condition of the monitored asset was correct in thepast based on the same or similar feature information being assessed bythe predictive algorithms used by the prediction/classification unit203.

The prognosis information may be adopted responsive to the confidencelevel exceeding a confidence level threshold (stage 1402). The user mayprovide service information, including diagnostic information, repairinformation, or both, that may indicate that the prognosis informationproduced by the prediction/classification unit 203 does not appear to becorrect. However, the data tagging unit 209 may disregard or discountservice information provided by the user that contradicts the prognosisinformation where the confidence level for the prognosis exceeds theconfidence level for the service information provided by the user. Theconfidence level associated with the service information may be based onattributes of the user, such as but not limited to an experience levelof the user, an amount of experience that the user has with theparticular type of monitored asset, how often the user has correctly orincorrectly has diagnosed conditions related to this monitored asset orcorrectly or incorrectly diagnosed conditions related to other monitoredassets, or other factors that may be relevant to the accuracy of thediagnosis provided by the user.

The data tagging unit 209 may also be configured to send a command tothe work order management unit 206 to cause the work order managementunit 206 to request that the user or another user reassess the status ofthe monitored asset 101. The work order management unit 206 may requestthat the user or another user perform repairs or maintenance that havebeen successful in the past for resolving the condition of the monitoredasset 101. The server 106 may then monitor the status of the asset todetermine whether the condition is subsequently resolved in response tothe repairs.

FIG. 15 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.14. The server 106 provides the means for implementing the processillustrated in FIG. 15. The example illustrated in FIG. 15 is an exampleonly and is not limiting.

The tagged feature information may be updated with a confidence levelassociated with the diagnostic information, the repair information, orboth (stage 1501). The service information provided by the user thatassesses the condition of the monitored asset 101 in response to theprognosis information may be assigned a confidence level by the workorder management unit 206 as discussed above. The data tagging unit 209may receive the confidence level information from the work ordermanagement unit 206 with the service information provided by the user.The data tagging unit 209 may include the confidence level informationwith the tagged feature information that is added to the training datarepository 207.

FIG. 16 is a diagram of an example of an additional stage that may beused to implement, at least in part, stage 704 of the processillustrated in FIG. 7. In the example illustrated in FIG. 16, data froma plurality of monitored assets may be obtained and used to train thepredictive algorithms used by the prediction/classification unit 203.The plurality of monitored assets may include more than one monitoredasset of a same or similar type. Including data from multiple assets ofthe same or similar type may help refine the predictive algorithms byproviding a richer data set. The server 106 provides the means forimplementing the process illustrated in FIG. 16. The example stageillustrated in FIG. 16 is an example only and is not limiting.

The predictive algorithm may be trained based on feature information andservice information associated with a second monitored asset of aplurality of assets in conjunction with producing the prognosisinformation for the first monitored asset (stage 1601). The data taggingunit 209 may tag data from a plurality of monitored assets and to addthe tagged features to the training data repository 207. The learningunit 208 may obtain the tagged feature information from the trainingdata repository 207 and train the predictive algorithms used by theprediction/classification unit 203. The tagged feature information maybe used to refine the predictive algorithms.

FIG. 17 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The sensor 102 providesthe means for implementing the process illustrated in FIG. 17. Theprocess illustrated in FIG. 17 is an example only and is not limiting.The example process may be altered, e.g. by having stages added,removed, rearranged, combined, performed concurrently, by having singlestages split into multiple stages, or a combination thereof.

One or more characteristics associated with a monitored asset may besensed to produce sensor data (stage 1701). The sensor(s) 308 may senseone or more characteristics of associated with a monitored asset 101.The sensor(s) 308 may include, but are not limited, magnetometers,accelerometers, light sensors, temperature sensors, fluid sensors,pressure sensors, optical sensors, radiation sensors, vibration sensors,chemical sensors, other types of sensors, or a combination thereof, maybe utilized depending on the type of monitored asset for which sensordata is being collected. The sensor 102 may include more than one sensor308, and may include more than one type of sensor.

Operating parameters for the monitoring system may be dynamicallyconfigured (stage 1702). The operating parameters of the sensor may bedynamically configured at least in part based on current known operatingconditions of the monitored asset, the current prognosis of themonitoring system or combinations thereof, hereafter referred to as“current operating conditions.” The current operating conditions mayfurther include a state of the monitored asset, a state of the sensor102, a state of the server 106, or a combination thereof. The sensor 102may dynamically modify the operating parameters of one or more componentof the sensor, such as the feature extraction unit 311, sensorconfiguration information 304 and/or sensor(s) 308 based onconfiguration information received from the server 106, such as in theexample process illustrated in FIG. 18. For example, the operatingparameters of the feature extraction unit 311 may select certainfeatures in stages 1703 and 1704. The sensor 102 may also dynamicallymodify the operating parameters of one or more components of the sensor102, such as the feature extraction unit 311, based on featureinformation extracted from the sensor data produced by the sensors 308,such as in the example process illustrated in FIG. 21.

Features may be extracted from the sensor data to produce extractedfeature information (stage 1703). The feature extraction unit 311 of thesensor 102 may analyze the sensor data and to extract featureinformation. The sensor(s) 308 may produce a large quantity of sensordata. Not all of the sensor data may be relevant for the server 106 togenerate prognosis information for the monitored asset 101.

A subset of the extracted feature information may be selected to produceactive feature information (stage 1704). The subset of the extractedfeatures may be selected at least in part based on current operatingconditions of the monitoring system. The feature extraction unit 311 mayimplement one or more feature selection algorithms for selecting whichfeatures to include in the active feature information that is providedto the server 106. The feature selection by the feature extraction unit311 may be influenced at least in part by feedback received from theserver in the form of configuration information for the sensor 102. Theserver 106 may generate prognosis information based on the featureinformation being provided by the sensor(s) 102 associated withmonitored asset(s) 101. The server 106 may send configurationinformation to the sensor(s) 102 that changes the features included inthe active feature information that is reported to the server 106. Forexample, the prediction/classification unit 203 or the learning unit 208may determine that a certain combination of features is indicative of aparticular condition occurring with a particular monitored asset or typeof monitored asset. The server 106 may send configuration information tothe sensor(s) 102 to request that the sensors provide certaincombinations of active feature information. This combination may beselected by the server 106 to include feature information that may beused to identify a set of most common conditions that typically occurwith the particular monitored asset or a particular type of monitoredasset. The feature extraction unit 311 of the sensor 102 may also builda dynamic baseline for a monitored asset 101 that may be used todetermine expected feature information for that monitored asset, and thefeature extraction unit 311 may include feature information for thatmonitored asset that has deviated from the dynamic baseline. FIG. 21provides an example process for determining such a dynamic baseline.

The active feature information may be sent to the server viacommunications network (stage 1705). The active feature information maybe sent to a server, such as the server 106, which may process thefeature information as discussed above. The server 106 may generateprognosis information for the monitored asset 101 associated with thefeature information, and to perform the various other processesdiscussed in the preceding examples.

FIG. 18 is a flow diagram of an example process for modifying theoperating parameters of a sensor according to the disclosure. The sensor102 provides the means for implementing the process illustrated in FIG.18. The process illustrated in FIG. 18 is an example only and is notlimiting. The example process may be altered, e.g. by having stagesadded, removed, rearranged, combined, performed concurrently, by havingsingle stages split into multiple stages, or a combination thereof. Theprocess illustrated in FIG. 18 may be used to implement, at least inpart, stage 1702 of the process illustrated in FIG. 17.

Configuration information may be received from the server (stage 1801).The server 106 may generate configuration information for the sensor102, and to send the configuration information to the sensor 102 via thenetwork 105. The sensor 102 may receive the configuration informationvia the network interface 301. The sensor 102 may store theconfiguration information in the memory 302 as the sensor configurationinformation 304. The sensor configuration information 304 may alsoinclude configuration information that was previously stored in thememory 302 and may include configuration information that was determinedat the sensor 102.

The operating parameters of the feature extraction component may bemodified according to the configuration information (stage 1802). Theprocessor 309 of the sensor 102 may modify the operating parameters ofthe sensor 102 according to the configuration information. The processor309 may configure the rate at which the sensor(s) 308 produce sensordata according to the configuration information. The processor 309 mayalso configure the rate at which feature extraction is performed on thesensor data by the feature extraction unit 311, a rate at which thefeature information is reported to the server 106, or combinationsthereof. The configuration information may also be used to configure thefeature extraction unit 311 to change the set of features extracted fromthe sensor data, to change the set of features included in the activefeature information reported to the server, to modify one or morefeatures, or a combination thereof. These examples do not limit theconfiguration information to these specific examples. The configurationinformation may be used to alter other operating parameters of thesensor 102 in addition to or instead of one or more of the examplesdiscussed herein.

FIG. 19 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The sensor 102 providesthe means for implementing the process illustrated in FIG. 19. Theprocess illustrated in FIG. 19 is an example only and is not limiting.The example process may be altered, e.g. by having stages added,removed, rearranged, combined, performed concurrently, by having singlestages split into multiple stages, or a combination thereof. The processillustrated in FIG. 19 may be used to implement at least in partadditional stages of the processes illustrated in FIG. 18 or FIG. 22.

One or more additional characteristics associated with the monitoredasset may be sensed to produce additional sensor data (stage 1901). Theoperating parameters of the sensor 102 may be changed, for example, dueto configuration information received from the server 106 or due toother changes that occur at the sensor. FIGS. 18 and 22 illustrate twoexample processes where the operating parameters of the sensor maychange. The sensor may continue monitoring the monitored asset 101 aftersuch a change in the operating parameters of the sensor 102.

Features may be extracted from the additional sensor data to producesecond extracted feature information (stage 1902). The featureextraction unit 311 may extract feature information from the additionalsensor data. The feature extraction unit 311 may determine at least asubset of the feature information to send to the server 106. The server106 may use this additional feature information to determine prognosisinformation for the monitored asset 101.

At least a subset of the second extracted feature information may beselected to produce second active feature information (stage 1903). Thefeature extraction unit 311 may implement one or more feature selectionalgorithms for selecting which features to include in the active featureinformation that is provided to the server 106. The feature selection bythe feature extraction unit 311 may be influenced at least in part bythe changes to the operating parameters of the sensor 102.

The second active feature information may be sent to the server via thecommunications network (stage 1904). The second active featureinformation may be sent to a server, such as the server 106, which mayprocess the feature information as discussed above. The server 106 maygenerate prognosis information for the monitored asset 101 associatedwith the second feature information, and to perform the various otherprocesses discussed in the preceding examples.

FIG. 20 is a diagram of an example of an additional stage that may beused to implement an additional stage of the process illustrated in FIG.18. The sensor 102 provides the means for implementing the additionalstage illustrated in FIG. 20. The example illustrated in FIG. 20 is anexample only and is not limiting.

A sensing rate at which sensor data is being produced by the monitoringsystem, a rate at which feature extraction is performed by themonitoring system, a rate at which feature information is sent to theserver or a combination thereof may be adjusted (stage 2001). Theconfiguration information received from the server 106 may be usedconfigure various operating parameters of the sensor 102, including butnot limited the sample rate at which the sensor data is produced by thesensor(s) 308, and the rate at which the feature information is providedto the server 106 by the sensor 102. The server 106 may also provideconfiguration information that specifies which features to include ornot include in the feature information that is provided to the server106 by the sensor 102.

FIG. 21 is a diagram of an example of an additional stage that may beused to implement at least in part, stage 1702 of the processillustrated in FIG. 17. The sensor 102 provides the means forimplementing the additional stage illustrated in FIG. 21. The stageillustrated in FIG. 21 is an example only and is not limiting.

A dynamic baseline value may be determined for one or more features(stage 2101). The dynamic baseline value represents an expected valuefor each of the one or more features over time. The feature extractionunit 311 may develop the dynamic baseline over time based on observedsensor data for a monitored asset. In some implementations, the featureextraction unit 311 may be provided with default dynamic baselineinformation that may be used to provide an initial expected baselinevalues before the sensor 102 has had time to observe a sufficient amountof information to produce a dynamic baseline for a monitored asset. Thedefault dynamic baseline information may be provided by the server 106,and may be based on attributes of the monitored asset, attributes of aplurality of similar monitored assets, expected environmental conditionsat the location where the monitored asset is located, other factors thatmay influence one or more characteristic of the monitored asset that maybe monitored by the sensor(s) 308. These other factors may includechanges in the characteristic(s) of the monitored asset 101 that aremonitored by the sensor(s) 308 that occur over time due to normal wearand tear experienced by the monitored asset 101. The dynamic baselinedetermined by the feature extraction unit 311 for this feature may takeinto account these changes in the expected value of the featureextracted from the sensor data over time. The server 106 may provideinformation as to expected changes in the patterns of thesecharacteristic(s) over time that have been generated by observing theoperation of the same or similar type of monitored asset over time. Thefeature extraction unit 311 may use this information when determiningthe dynamic baseline for the monitored asset.

In an example implementation to illustrate this concept, the featureextraction unit 311 may a feature related to a temperature of themonitored asset 101. The ambient temperature of the monitored asset 101may vary over time. For example, the monitored asset 101 may be locatedin an outdoor area where the ambient temperature may vary over thecourse of the year due to the change in seasons and over the course ofthe day due to changes in temperature between day and night. The featureextraction unit 311 may obtain sensor information over time thatreflects these changes and may use these to develop a dynamic baselinemodel for the temperature of the monitored asset 101. A similar approachmay be used with other characteristics of the monitored asset 101.Furthermore, as discussed above, default information may be provided bythe server that may serve as an initial baseline that may be adapted bythe feature extraction unit 311 as sensor data is collected for amonitored asset 101 over time.

FIG. 22 is a flow diagram of an example process for modifying theoperating parameters of a sensor according to the disclosure. The sensor102 provides the means for implementing the process illustrated in FIG.22. The process illustrated in FIG. 22 is an example only and is notlimiting. The example process may be altered, e.g. by having stagesadded, removed, rearranged, combined, performed concurrently, by havingsingle stages split into multiple stages, or a combination thereof. Theprocess illustrated in FIG. 19 may be used to implement at least in partadditional stages of the process illustrated in FIG. 21.

A deviation from the dynamic baseline value may be identified for afeature of the one or more features (stage 2201). The feature extractionunit 311 may determine a particular feature has deviated from anexpected dynamic baseline value for that feature. The feature extractionunit 311 may identify such a deviation in response to the feature valueextracted from the sensor data falling outside of an expected range fromthe dynamic baseline value for that feature. The feature extraction unit311 may determine that one or both of these features has deviated fromthe dynamic baseline by more than a predetermined amount. Each type offeature information may be associated with a predetermined thresholdindicative of a deviation from the dynamic baseline value for that typeof feature.

Referring back to the to pump example discussed above with respect toFIG. 3, the feature extraction unit 311 in this example is configured toextract feature information related to: (1) flow rate from the pump and(2) vibration data from at least one point on the housing of the pump.The feature extraction unit 311 is configured to produce a dynamicbaseline over time that represents expected values of the flow rate andvibration level data features over time. The feature extraction unit 311may determine that the flow rate has deviated from the baseline wherethe flow is lower than or higher than the expected flow rate value ofthe dynamic baseline by more than a predetermined threshold or by apredetermined percentage. The feature extraction unit 311 may determinethat the vibration level of the pump has deviated from the expectedvibration level value of the dynamic baseline where the vibration levelexceeds the expected vibration level by more than a predeterminedthreshold or by a predetermined percentage. This example illustratesconcepts disclosed herein and does not limit the monitored asset to apump or the features extracted by the feature extraction unit 311 tothese specific features.

The operating parameters of the feature extraction unit may be modifiedto include the identified feature in the second active featureinformation (stage 2202). Such deviations from the dynamic baselinevalue for a monitored asset 101 may be indicative of a condition of themonitored asset that may require repairs or maintenance. The sensor 102may include such features in the feature information that is provided tothe server in stage 1704 of the process illustrated in FIG. 17.Furthermore, the server 106 may provide feedback to the sensor 102 thatincludes configuration information for changing one or more operatingparameters of the sensor 102, such as that discussed in the exampleprocesses illustrated in FIGS. 18 and 20 and other examples discussed inthe preceding examples.

FIG. 23 is an example state diagram illustrating an example of variousstates that a sensor, such as the sensor 102, may transition betweenaccording to the disclosure. The state diagram illustrated in FIG. 23 isan example only and is not limiting. The example state may be altered,e.g. by having states added, removed, rearranged, combined, performedconcurrently, by having single states split into multiple states, or acombination thereof.

The sensor 102 may enter into a sleep state 2301. The sensor 102 mayenter into the sleep state periodically to save power, particularlywhere the sensor 102 is battery powered. In some implementations, thesensor 102 may not enter into a sleep state. In particular, the sensor102 may not enter into the sleep state where the sensor 102 has anexternal power source and is not reliant on a battery or other onboardpower source that may be exhausted.

The sensor 102 may enter into the configure and modify state 2305 fromthe sleep state 2301. The sensor 102 may enter into the configure andmodify state 2305 in response to receiving sensor configurationinformation from the server 106. The server 106 may send sensorconfiguration information to the sensor 102. The sensor 102 may use toreconfigure one or more operating parameters of the sensor 102 withoutrequiring the sensor 102 to be manually reconfigured or replaced. Thesensor 102 may configure the rate at which the sensor(s) 308 producesensor data according to the configuration information. The sensor 102may also configure the rate at which feature extraction is performed onthe sensor data, a rate at which the feature information is reported tothe server 106, or combinations thereof. The configuration informationmay also be used to change the set of features extracted from the sensordata, to change the set of features included in the active featureinformation reported to the server, to modify one or more features, or acombination thereof. These examples do not limit the configurationinformation to these specific examples. The configuration informationmay be used to alter other operating parameters of the sensor 102 inaddition to or instead of one or more of the examples discussed herein.The sensor 102 may return to the sleep state from the configure andmodify state 2305. In implementations where the sensor 102 is notconfigured to enter into the sleep state, the sensor 102 may enter intothe configure and modify state 2305 in response to receiving sensorconfiguration information from the server 106 while in another of thestate illustrated in FIG. 23.

The sensor 102 may enter into a sense and collect state 2302 from thesleep state 2301. The sensor 102 may transition from the sleep state tothe sense and collect state 2302 in which the sensor(s) 308 of thesensor 102 are configured to produce sensor data. The sensor 102 maytransition from the sleep state 2301 to the sense and collect state 2302in response to an event or in response to the expiration of a timer. Thesensor 102 may respond to certain types of events, such as a wakeupsignal or sensor configuration information being received from theserver 106, a signal received from a technician or other user, or anevent occurring at the monitored asset 101 for which the sensor 102 isconfigured to exit the sleep state 2301. The sensor 102 may remain inthe sense and collect state 2302 for a predetermined period of time,based on the sensor data being collected by the sensors(s) 308 of thesensor 102, based on an amount of data collected by the sensor(s), basedon sensor configuration information received from the server, or acombination of one or more of these factors. The sensor(s) 308 maymeasure a single characteristic of the monitored asset 101 or theoperating environment in which the monitored asset 101 is disposed, suchas temperature or humidity. Some sensor(s) 102 may include more than onesensor 308. Other types of sensors may be included, such as but notlimited to accelerometers, gyroscopes, infrared or ultraviolet sensors,and acoustic sensors.

The sensor 102 may transition from the sense and collect state 2302 toan encode and transmit state 2304 or an extract features state 2303. Thesensor 102 may transition to the encode and transmit state 2304 from thesense and collect state 2302 where the sensor 102 is configured totransmit raw sensor data to the server 106. The server 106 may send arequest to the sensor 102 for raw sensor data or configurationinformation obtained from the server 106 may indicate that the sensor102 should transmit raw sensor data to the server 106. The sensor 102may process the raw sensor data before transmitting the raw sensor datato the server 106. For example, the sensor 102 may compress the rawsensor data, to encrypt the raw sensor data, to perform otherpreprocessing on the raw sensor data, or a combination thereof.

While in the extract features states 2303, the sensor 102 may extractfeature information from the sensor data obtained by the sensor(s) 308of the sensor. The features included in the feature information may beused as classification variables by the classification aspect of thepredictive algorithms of the server 106. When training the predictivealgorithms, the predictive algorithms may be provided with tagged orlabeled feature information that has been associated with a label thatindicates that the feature information is indicative of a possiblecondition or imminent occurrence of the possible condition at themonitored asset 101. Once the predictive algorithms have been trained,the sensor 102 may provide untagged feature information from thesensor(s) 102 associated with a monitored asset, and the predictivealgorithms may attempt to determine whether the monitored asset 101 isexperiencing a possible condition or the possible condition is imminent.The feature information may be extracted from the sensor data usingalgebraic expressions, statistical analysis, or a combination thereof.Some examples of the types of classification variables of interest thatmay be extracted as feature information associated with vibration of themonitored asset 101 include but are not limited to the Root Mean SquareAmplitude (RMS) of the vibration, the crest factor of the vibration, theshape factor of the vibration, the mean point of the vibration, theskewness of the vibration, the kurtosis of the vibration, and/or othersuch aspects of the vibration that may be computing in a time orfrequency domain. Other types of feature information may be extractedfrom the sensor data based on the type of sensor data being analyzed forfeature extraction.

The feature information produced by the sensor is used to by predictivealgorithms of the server 106 to determine whether there is a fault orchange in condition with the monitored asset 101 that may requiremaintenance or repairs to be performed on the monitored asset 101.Performing the feature extraction at the sensor 102 and analyzing thefeature information at the server 106 may provide an advantage oversystems which rely on the sensor to diagnose a fault or other conditionfor the device, because the predictive algorithms may combine featureinformation received from multiple sensors associated with the monitoredasset 101 to determine the prognosis information. To illustrate thisconcept, in a conventional system, a rotor may have a first sensor andsecond sensor disposed at opposite ends of the rotor. Relying on sensorinformation at the first or the second sensor may not be sufficient toclearly identify an imbalance condition with the rotor. However, in anexample implementation of techniques disclosed herein, a first sensor102 at one end of the rotor and a second sensor 102 at the opposite endof the rotor. The first and second sensors may provide featureinformation to the server 106, which may include information such asphase differences detected by each of the sensors 102. The combinationof the feature information from the first and second sensors 102 may besufficient for the predictive algorithms to recognize that an imbalancecondition has occurred with the rotor. The use of multiple sensors thatmay also comprise more than one type of sensor 308 may provide more amore robust set of feature information that may be used by thepredictive algorithms and may also be used to provide a more robust setof tagged feature information that may be used to refine the predictivealgorithms used by the server as the prognosis information generated bythe server 106 is validated with service information provided by userswho assess the condition of the monitored asset 101 in response to theprognosis information.

The sensor 102 may transition from the extract features state 2303 tothe encode and transmit state 2304 where the feature informationextracted by the sensor 102 is transmitted to the server 106 via thewireless network interface of the sensor 102. The sensor 102 may processthe feature information before transmitting the feature information tothe server 106. For example, the sensor 102 may compress the featureinformation, to encrypt the feature information, to perform otherprocessing on the feature information, or a combination thereof. In someimplementations, the sensor 102 may identify a subset of featureinformation that includes data that appears to be more significant. Forexample, a subset of the feature information in which the feature valuesdeviated from the expected baseline values for the features. Othercriteria may also be used to determine which subset of the featureinformation to select for transmission to the server 106. For example,the sensor 102 may report 10 highest dominant samples for a particulartype of sensor data. The sensor 102 may be further configured toidentify that the subset of these samples that contain most of thedetails of interest, then that subset may be selected. For example, iffour of the samples include 99% of the energy of the ten samples, thenthe just those four samples may be transmitted to the server 106. Thisapproach may reduce power consumption by the device and reduce theamount of data being transmitted by the sensors 102. In someenvironments, hundreds or thousands of such devices may be deployed.After transmitting the feature information to the server 106, the sensor102 may transition back to the sleep state 2301.

FIG. 24 is a block diagram of an example operating environment 2400 thatcan be used to implement various techniques disclosed herein. Theexample operating environment includes a monitored asset 101 and amagnet 2402 affixed to the monitored asset 101. The monitored asset 101and the magnet 2402 are disposed in a non-ferromagnetic liquid,semiliquid, semisolid, or solid medium 2401 through which the sensor 102can be configured to sense changes in intensity of a magnetic field ofthe magnet 2402 and to produce sensor data based on the sensed changesin intensity of the magnetic field. The magnet 2402 may be, for example,a permanent magnet, an electromagnet, etc. An electromagnet may beconfigured to be powered by the monitored asset 101.

The sensor 102 is disposed outside of the non-ferromagnetic medium. Thesensor 102 includes at least one sensor 308 that is configured to sensechanges in intensity of the magnetic field of the magnet 2402. Thesensor(s) 308 can include but not limited to inductive pickup coils,Hall effect sensors, microelectromechanical systems (MEMS) magneticfield sensors, or other types of sensors capable of detecting changes inintensity of the magnetic field. An inductive pickup coil sensor cangenerate an electromotive force (EMF) in response to movement of themagnet relative to the sensor 308. A linear Hall effect sensor isconfigured to generate an output voltage that is proportional to themagnetic field passing through the Hall effect sensor. The outputvoltage increases with an increase in intensity of the magnetic fieldpassing through the sensor and decreases with a decrease in intensity ofthe magnetic field passing through the sensor. The voltage of the outputsignal from the Hall effect sensor increases, for example, as the magnetaffixed to the monitored asset 101 moves closer to the sensor, and thevoltage of the output signal from the Hall effect sensor decreases asthe magnet affixed to the monitored asset 101 moves further away fromthe sensor 308. The sensor(s) 308 can also detect motions of the magnetorthogonal to the sensing direction or vector of the sensor(s) 308, dueto the magnetic field pattern associated with magnet 2402. Thus, sensor308 can be used to detect movements of the monitored asset 101 relativeto the sensor of the sensor 102. Other types of sensors, such as but notlimited to a MEMS magnetic field sensor can also be used to detectchanges in intensity of the magnetic field of the magnet 2402 indicativeof movement of the magnet 2402 relative to the sensor.

The sensor 102 can be configured to use the sensor data to monitorvibration of the monitored asset 101. As the monitored asset 101vibrates, the position of the magnet affixed to the monitored assetchanges relative to the sensor(s) 308 of the sensor 102. The magneticfield sensor(s) of the sensor can be configured to detect changes inintensity of the magnetic field produced by the magnet 2402 affixed tothe monitored asset 101. The sensor data obtained by the sensor(s) ofthe can be used by the feature extraction unit 311 to characterizevibrations of the monitored asset 101 and to extract feature informationfrom the sensor data that may be indicative of a problem with themonitored asset. The feature information can be sent to the server 106of the monitoring system which is configured to analyze the featureinformation using one or more predictive algorithm(s) to produceprognosis information for the monitored asset 101, as discussed in thepreceding examples. The prognosis information can be indicative of aproblem with the monitored asset 101 for which repair and/or maintenanceis desirable.

A technician or other user may wish to calibrate and/or configure thesensor(s) 308 of the sensor 102 to detect the magnetic field of themagnet 2402. Some implementations, such as that illustrated in FIG. 26,can also include multiple magnets affixed to the monitored asset 101.The sensor(s) 308 of the sensor 102 may need to be positioned within acertain distance of the magnet(s) affixed to the monitored asset and mayneed to be placed in a certain position relative to the magnet(s). Forexample, where the sensor comprises a Hall effect sensor or an inductivecoil pickup sensor, the sensor may need to be placed such that a sensingdirection of the sensor is aligned or partially aligned to the polarityof the magnet for which changes to the magnetic field are to be sensed.

FIG. 25 is a block diagram of an example operating environment 2500 thatcan be used to implement various techniques disclosed herein. Theexample operating environment 2500 depicts an example implementation inwhich the monitored asset 101 is a pump submerged in liquidnon-ferromagnetic medium. A sensor 2503 of a sensor 102 is positionedabove a fluid line 2501 of a fluidic medium in which the pump issubmerged. The sensor 102 is omitted from the figure for the sake ofclarity. However, the sensor 2503 may be a sensor 308 and can beintegrated into the sensor 102 or may be external to and communicablycoupled with the sensor 102.

The sensor 2503 can be used to detect changes in intensity of themagnetic field of the magnet 2502 which can be indicative of vibrationof the monitored asset 101. Vibration of the monitored asset 101 may beindicative of certain conditions of the monitored asset 101 for whichrepair and/or maintenance of the monitored asset 101 is desirable.

In the example operating environment illustrated in FIG. 25, the sensor2503 comprises an inductive pickup coil. However, the techniquesdisclosed herein are not limited to sensors comprising an inductivepickup coil. Other types of sensors can be used, including but notlimited to Hall effect sensors, microelectromechanical systems (MEMS)magnetic field sensors, or other types of sensors capable of detectingchanges in intensity of the magnetic field of the magnet 2502 resultingfrom vibrations of the monitored asset 101 to which the magnet 2502 isaffixed.

An electromotive force (EMF) is induced in the inductive pickup coil ofthe sensor 2503 in response to movement of the magnet 2402 relative tothe sensor 2503. The magnetic field of the magnet 2502 mounted on themonitored asset 101 can remain constant. Movement of the magnet 2502resulting from vibrations of the monitored asset 101 can cause themagnet 2502 and the magnetic field generated by the magnet 2502 to moverelative to the sensor 2503. This motion can induce a voltage in thecoil of the sensor 2503. The sensor 2503 can produce an output signalrepresentative of these changes in voltage, and the sensor 102 can usethis information to determine a velocity of the monitored asset 101 at aparticular point in time.

The feature extraction unit 311 of the sensor 102 can be configured toanalyze the sensor data produced by the sensor 2503 to create featureinformation. The feature extraction unit 311 of the sensor 102 can beconfigured to analyze the sensor data produced by the sensor 2503 toproduce a dynamic baseline representing expected vibrations of themonitored asset 101 over time. The feature extraction unit 311 can beconfigured to compare feature information extracted from the sensor dataof the sensor 2503 to the expected values for each of these featuresrepresented in the dynamic baseline and detect deviations from theexpected baseline value. Such deviations from the dynamic baseline canbe indicative of a problem developing in the pump or other monitoredasset 101. The sensor 102 can be configured to send feature informationextracted from the sensor data to the server 106 for analysis by thepredictive algorithm(s) to make a determination whether the monitoredasset 101 may be experiencing a known condition for which the predictivealgorithm(s) have been trained to recognize and for which repair and/ormaintenance of the monitored asset 101 may be desirable.

FIG. 26 is a block diagram of an example operating environment 2600 thatcan be used to implement various techniques disclosed herein. Theexample operating environment 2600 is similar to the example operatingenvironment 2400 illustrated in FIG. 24 except multiple magnets areattached to the monitored asset 101. Multiple magnets can be used toprovide more robust information regarding the vibration or other motionof the monitored asset 101.

In an embodiment, the magnet 2602 a is mounted at one position on themonitored asset and oriented along a first axis relative to themonitored asset 101, and the magnet 2602 b is mounted at a secondposition on the monitored asset and oriented along a second axis (e.g.orthogonal to the first axis) relative to the monitored asset 101. Thesensor 102 is located outside of the non-ferromagnetic medium 2601 inwhich the monitored asset 101 is disposed. The medium 2601 may be aliquid, semiliquid, semisolid, or solid as in the preceding examples.

The sensor 102 includes a first sensor 308 a configured to sense changesin intensity of the magnetic field of the first magnet 2602 a, and asecond sensor 308 b configured to sense changes in intensity of themagnetic field of the second magnet 2602 b. The arrow 2608 a indicates adirection of sensitivity of the first sensor 308 a, and the arrow 2608 bindicates a direction of sensitivity of the second sensor 308 b. Thefirst sensor 308 a can be positioned relative to the first magnet 2602 asuch that the first sensor 308 a can detect changes in intensity of themagnetic field of the first magnet 2602 a resulting from movements ofthe magnet 2602 a. The second sensor 308 b can be positioned relative tothe second magnet 2602 b such that the second sensor 308 b can detectchanges in intensity of the magnetic field of the second magnet 2602 bresulting from movements of the second magnet 2602 b. In someimplementations, the orientation of the sensors 308 and the magnets 2602a and 2602 b can be such that first sensor 308 a is not able to detectchanges in intensity of the magnetic field associated with the secondmagnet 2602 b and that second sensor 308 b is not able to detect changesin intensity of the magnetic field associated with the first magnet 2602a. In other implementations, the orientation of the sensors 308 andmagnets 2602 can be such that first sensor 308 a is able to detectchanges in intensity of the magnetic field associated with the secondmagnet 2602 b with a response that is substantially less than (such asbut not limited to one-tenth or one-one hundredth) the response of firstsensor 308 a to changes in intensity of the magnetic field of the magnet2602 a. Similarly, the orientation of the sensors 308 and magnets 2602can be such that second sensor 308 b is able to detect changes inintensity of the magnetic field associated with the first magnet 2602 awith a response that is substantially less than (such as but not limitedto one-tenth or one-one hundredth) the response of second sensor 308 bwith regard to changes in intensity of the magnetic field of the magnet2602 b.

This configuration allows the sensor 102 to generate richer featureinformation for the monitored asset 101, because the sensor 102 is ableto obtain vibration data for the monitored asset 101 along two separateaxes. The feature extraction unit 311 can be configured to extractfeatures from the sensor data generated by the respective sensorsconfigured to sense changes in the respective magnetic fields of themagnets 2602 a and 2602 b. While the example illustrated in FIG. 26includes two magnets, other implementations can include additionalmagnets oriented along different axes and the sensor 102 can includesensors oriented to detect changes in intensity of the magnetic fields arespective one of these magnets. Generating feature information alongmultiple axes can provide a more nuanced data that can provide greaterdetail about the vibration of the monitored asset, which can be used bythe predictive algorithm(s) of the server to provide more accurateprognosis information for the monitored asset. For example, the phaserelationship between the two or more sensor outputs can be compared. Thephase relationship can be used to determine whether the monitored asset101 is moving in a more complex manner. For example, the phaserelationship can indicate whether the vibration of monitored asset 101is rotational instead translational. In an example implementation, themonitored asset 101 is a pump that has multiple magnets affixed to thepump. The sensor 102 can be configured to produce sensor data for eachof the magnets and to examine the phase relationship between the datacollected from each of the magnets. In the pump example, such acomparison could reveal that the pump vibration is rotational which canindicate a possible shaft misalignment of the pump.

The feature extraction unit 311 of the sensor 102 can be configured todetermine a dynamic baseline for the monitored asset 101 representingexpected vibrations of the monitored asset 101 along the first andsecond axes.

In some implementations, more than one sensor 102 can be used ratherthan the sensor 102 including multiple sensors configured to sensechanges in a magnet affixed to the monitored asset 101. The multiplesensor 102 may be appropriate based on the size, shape, or location ofthe monitored asset 101. The server 106 can be configured to combine thefeature information extracted by the multiple sensor 102 where multiplesensors 102 are configured to monitor the same monitored asset 101.

FIG. 27 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The sensor 102 providesthe means for implementing the process illustrated in FIG. 27. Theprocess illustrated in FIG. 27 is an example only and is not limiting.The example process can be altered, e.g. by having stages added,removed, rearranged, combined, performed concurrently, by having singlestages split into multiple stages, or a combination thereof.

The sensor 102 can sense changes in intensity of the magnetic field of amagnet 2402 affixed to the monitored asset 101 to produce sensor data(stage 2701). The sensor 102 can be configured to collect sensor datafrom one or more sensor(s) 308, which is disposed in a non-ferromagneticliquid or solid medium. As discussed in the preceding examples, thesensor(s) 308 can include one or more sensors configured to sensechanges in intensity of a magnetic field of a magnet field of the magnet2402 affixed to the monitored asset 101. The sensor(s) 308 can beconfigured to produce sensor data that can be analyzed to generatefeature information that can be provided to the server 106 for analysisby one or more predictive algorithm(s). The changes in intensity of themagnetic field can represent a vibration pattern of the monitored asset101, which can be indicative of a condition of the monitored asset 101requiring repair or maintenance.

Vibration data for the monitored asset can be produced by analyzing thechanges in intensity of the magnetic field of the magnet associated withthe monitored asset (stage 2702). The feature extraction unit 311 of thesensor 102 can be configured to analyze the sensor data provided by thesensor(s) 308 of the sensor 102 to produce feature information that isindicative of the vibration of the monitored asset 101. The featureinformation can be provided to the server 106 for analysis by thepredictive algorithm(s) as discussed in the preceding examples.

FIG. 28 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The sensor 102 providesthe means for implementing the process illustrated in FIG. 28. Theprocess illustrated in FIG. 28 is an example only and is not limiting.The example process can be altered, e.g. by having stages added,removed, rearranged, combined, performed concurrently, by having singlestages split into multiple stages, or a combination thereof.

The sensor 102 can sense changes in intensity of the magnetic field of amagnet 2402 affixed to the monitored asset 101 to produce sensor data(stage 2801). The sensor 102 can be configured to collect sensor datafrom one or more sensor(s) 308, which is disposed in a non-ferromagneticliquid or solid medium. As discussed in the preceding examples, thesensor(s) 308 can include one or more sensors configured to sensechanges in intensity of a magnetic field of the magnet 2402 affixed tothe monitored asset 101.

The sensor data can be analyzed to produce feature informationindicative of the vibration of the monitored asset (stage 2802). Thefeature extraction unit 311 of the sensor 102 can be configured toanalyze the sensor data provided by the sensor(s) 308 of the sensor 102to produce feature information that is indicative of the vibration ofthe monitored asset 101. The feature information can be provided to theserver 106 for analysis by the predictive algorithm(s) as discussed inthe preceding examples.

The feature data can be provided to a predictive algorithm to generateprognosis information indicating an occurrence of a known condition ofthe monitored asset (stage 2803). The feature extraction unit 311 of thesensor 102 can be configured to send feature information to the server106 for features that deviated from the dynamic baseline of vibrationdata established for the monitored asset 101. As discussed in thepreceding examples, the predictive algorithm(s) of the server 106 cananalyze the feature information to determine whether there is a fault orchange in condition with the monitored asset 101 for which it may bedesirable to perform maintenance and/or repair on the monitored asset101.

FIG. 29 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The process illustratedin FIG. 29 can be used to implement, at least in part, stages 2801 and2802 of the process illustrated in FIG. 28. The sensor 102 provides themeans for implementing the process illustrated in FIG. 29. The processillustrated in FIG. 29 is an example only and is not limiting. Theexample process can be altered, e.g. by having stages added, removed,rearranged, combined, performed concurrently, by having single stagessplit into multiple stages, or a combination thereof.

A signal value can be determined based on changes in intensity of themagnetic field (stage 2901). The type of signal value determined isbased on the type of sensor 308 of the sensor 102 that is used to sensethe changes in intensity of the magnetic field of the magnet. Forexample, where the sensor 308 is an inductive pickup coil sensor, suchas that discussed in the example illustrated in FIG. 25, the sensor 308of the sensor 102 can be configured to detect changes in intensity ofthe magnetic field of a magnet affixed to a monitored asset usingelectromagnetic induction. Electromagnetic induction is the productionof an electromotive force (EMF) across an electrical conductor in achanging magnetic field. Faraday's law of induction predicts how achanging magnetic field will interact with an electric circuit toproduce an EMF. The changing magnetic field induces proportionate EMF ina sensor. For example, referring back to the example illustrated in FIG.25, the coil of the sensor 2503 is aligned to the polarity of themagnetic field of the magnet 2502 affixed to the monitored asset 101. Inimplementations where the sensor 308 is a Hall effect sensor, the sensoris configured to generate an output voltage that is proportional to themagnetic field passing through the Hall effect sensor. The outputvoltage increases with an increased magnetic field passing through thesensor and decreases with a decreased magnetic field passing through thesensor. Other types of sensors may produce other types of outputs inresponse to changes to sensing changes to the magnetic field of themagnet affixed to the monitored asset 101. The sensor 102 can beconfigured to include more than one type of sensor 308, and can includemore than one type of sensor configured to detect changes in intensityof the magnetic field of the magnet affixed to the monitored asset 101.

The feature extraction unit 311 of the sensor 102 can be configured todetermine vibration features based on the signal value(s) of the sensordata collected by the magnetic sensor(s) 308 of the sensor 102.Vibrations of the monitored asset 101 can cause the magnet to moverelative to the sensor(s) 308 of the sensor 102. The displacement of themagnet due to vibration causes a change in magnetic flux measured by theinduction coil magnetometer in the example illustrated in FIG. 25.However, as noted in the preceding examples other types of sensors, suchas but limited to Hall effect sensors, microelectromechanical systems(MEMS) magnetic field sensors, or other types of sensors capable ofdetecting changes in intensity of the magnetic field can alternativelybe used.

A velocity of the monitored asset 101 can be determined (stage 2902). Inimplementations where an inductive pickup coil sensor is used, thevelocity can be determined based on the EMF sensed by the sensor. TheEMF is proportional to the rate of change of flux which is proportionalto rate of change of displacement. Thus, the velocity of the magnet, andtherefore the asset, is proportional to the EMF. Further, theacceleration of the surface to which the magnet 2502 is affixed isproportional to the rate of change of the EMF. Thus, the induction coilmagnetometer can sense the velocity and acceleration of the vibratingsurface. In implementations where a Hall effect sensor is used, a rateof change of the output voltage of the sensor can be used to determine avelocity of the monitored asset 101. The techniques disclosed herein arenot limited a particular type of sensor, and the relationships betweenthe position, velocity, and/or acceleration of the magnet (or themagnetic flux thereof) will vary depending upon the type of sensor beingused. For example, the preceding examples that utilized an inductivefield coil sensor versus a Hall effect sensor are indicative of some ofthese differences.

FIG. 30 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The process illustratedin FIG. 30 can be used to implement, at least in part, stage 2801 of theprocess illustrated in FIG. 28. The sensor 102 provides the means forimplementing the process illustrated in FIG. 30. The process illustratedin FIG. 30 is an example only and is not limiting. The example processcan be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof. The example processillustrated in FIG. 30 can be used with the example implementationillustrated in FIG. 26 in which multiple magnets can be affixed to themonitored asset 101.

Changes in intensity of a first magnetic field associated with a firstmagnet 2602 a associated affixed to the monitored asset can be sensed(stage 3001). The sensor 102 can include a first sensor 308 a configuredto sense changes in intensity of the magnetic field of the first magnet2602 a. The first magnet 2602 a and the second magnet 2602 b can beoriented relative to one anther such that the polarities of the twomagnets are distinguishable from one another by the sensor(s) 308 of thesensor. A first sensor of the sensor(s) 308 can be oriented such thatthe first sensor can detect changes in intensity of the first magneticfield associated with the first magnet. The first sensor can beconfigured to produce sensor data that represents movements of themonitored asset 101 along a first axis.

Changes in intensity of a second magnetic field associated with a secondmagnet 2602 b associated affixed to the monitored asset can be sensed(stage 3002). A second sensor of the sensor(s) 308 can be oriented suchthat the second sensor does not detect changes in intensity of the firstmagnetic field associated with the first magnet. The second sensor canbe configured to produce sensor data that represents movements of themonitored asset 101 along a second axis. The sensor data collected bythe first and second sensors can be stored in the memory 302 of thesensor 102 as sensor data 303. The feature extraction unit 311 can beconfigured to determine a phase relationship between the two or moresensor outputs of the first and second sensors. The phase relationshipcan be used to determine whether the monitored asset 101 is moving in amore complex manner. For example, the phase relationship can indicatewhether the monitored asset 101 has a rotational motion or atranslational motion. The sensor 102 can be configured to produce sensordata for each of the magnets and to examine the phase relationshipbetween the data collected from each of the magnets. The featureextraction unit 311 can be configured to extract feature informationfrom this sensor data and the phase relationship information determinedtherefrom. The feature information is based on sensor data representingmovements of the monitored asset along multiple axes, which can providemore precise representation of the vibration being experienced by themonitored asset 101 than in implementations where a single magnet isaffixed to the monitored asset.

FIG. 31 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The process illustratedin FIG. 31 can be used to implement additional stages of the processillustrated in FIG. 30. The sensor 102 provides the means forimplementing the process illustrated in FIG. 31. The process illustratedin FIG. 31 is an example only and is not limiting. The example processcan be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof.

A first signal value can be determined based on changes in intensity ofthe magnetic field associated with a first magnet 2602 a (stage 3101).The type of signal value determined is based on the type of sensor 318of the sensor 102 that is used to sense the changes in intensity of themagnetic field of the first magnet. Vibrations of the monitored asset101 along a first axis can cause the first magnet 2602 a to changeposition relative to the first sensor of the sensor 102 to output afirst signal value. As discussed in the preceding examples, differenttypes of sensor(s) 318 can be used to sense the changes in intensity ofthe magnetic field, and the output of the sensor data can vary based onthe type of sensor(s) 318 of the sensor 102.

A second signal value can be determined based on the changes inintensity of the magnetic field associated with the second magnetaffixed to the monitored asset 101 (stage 3102). As discussed above,vibrations of the monitored asset 101 along a second axis can cause thesecond magnet 2602 b to change position relative the second sensor ofthe sensor 102 to output a second signal value. The feature extractionunit 311 of the sensor 102 can be configured to determine the first andsecond signal values.

FIG. 32 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The process illustratedin FIG. 32 can be used to implement additional stages of the processillustrated in FIG. 31. The sensor 102 provides the means forimplementing the process illustrated in FIG. 32. The process illustratedin FIG. 32 is an example only and is not limiting. The example processcan be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof.

A first velocity of the monitored asset along a first axis can bedetermined (stage 3201). In implementations where an inductive pickupcoil sensor is used, the velocity can be determined based on the EMFsensed by the sensor. The EMF is proportional to the rate of change offlux which is proportional to rate of change of displacement. Thus, thevelocity of the magnet, and therefore the asset, is proportional to theEMF. Further, the acceleration of the surface to which the magnet 2502is affixed is proportional to the rate of change of the EMF. Thus, theinduction coil magnetometer can determine the velocity and accelerationof the vibrating surface. In implementations where a Hall effect sensoris used, a rate of change of the output voltage of the sensor can beused to determine a velocity of the monitored asset 101. The techniquesdisclosed herein are not limited a particular type of sensor, and therelationships between the position, velocity, and/or acceleration of themagnet (or the magnetic flux thereof) will vary depending upon the typeof sensor being used. For example, the preceding examples that utilizedan inductive field coil sensor versus a Hall effect sensor areindicative of some of these differences.

A second velocity of the monitored asset along a second axis can bedetermined (stage 3202). How the second velocity of the monitored assetis determined is dependent upon the type of sensor that is used asdiscussed above with respect to stage 3201. The velocity informationalong the first and second axes can be used to determine a dynamicbaseline value for the monitored asset 101. Once the dynamic baselinefor the monitored asset 101 has been established, the velocityinformation along the first and second axes can be used to determinewhether the vibration of the monitored asset 101 has deviated from theexpected values of the dynamic baseline.

FIG. 33 is a flow diagram of an example process for operating amonitoring system according to the disclosure. The process illustratedin FIG. 33 can be used to implement additional stages of the processillustrated in FIG. 28. The sensor 102 provides the means forimplementing the process illustrated in FIG. 33. The process illustratedin FIG. 33 is an example only and is not limiting. The example processcan be altered, e.g. by having stages added, removed, rearranged,combined, performed concurrently, by having single stages split intomultiple stages, or a combination thereof.

A dynamic baseline value for the one or more features can be determinedbased on the feature information (stage 3301). The feature extractionunit 311 of the sensor 102 can be configured to determine a dynamicbaseline value for each of the features that the feature extraction unit311 extracts from the sensor data. The dynamic baseline value representsan expected value for each of the one or more features over time, whichcan include but is not limited to expected vibration and/or othermovement of the monitored asset 101. The dynamic baseline can alsorepresent expected changes in the vibration of the monitored asset 101due to normal wear and tear as the monitored asset 101 ages. Byincluding such expected changes due to normal wear and tear into thebaseline, the sensor 102 can avoid reporting normal changes in theoperating condition of the monitored asset 101 as the device ages.However, unexpected deviations from this baseline value would bereported to the server 106, because such deviations could be indicativeof a condition for which repair and/or maintenance may be desirable. Thefeature extraction unit 311 of the sensor 102 can also be configured todetermine a dynamic baseline for the monitored asset 101 representingexpected vibrations of the monitored asset 101 along multiple axes wheremultiple magnets are affixed to the monitored asset 101, such as in theexample implementation illustrated in FIG. 26.

A deviation from the dynamic baseline value for a feature of the one ormore features can be identified (stage 3302). The feature extractionunit 311 can be configured to determine a particular feature hasdeviated from an expected dynamic baseline value for that feature. Thefeature extraction unit 311 can be configured to identify such adeviation in response to the feature value extracted from the sensordata falling outside of an expected range from the dynamic baselinevalue for that feature.

The feature information for the feature for which the deviation wasidentified can be provided to a server for analysis by predictivealgorithm(s) (stage 3303). The feature extraction unit 311 of the sensor102 can be configured to send feature information to the server 106 forfeatures that deviated from the dynamic baseline of vibration dataestablished for the monitored asset 101. As discussed in the precedingexamples, the predictive algorithm(s) of the server 106 can analyze thefeature information to determine whether there is a fault or change incondition with the monitored asset 101 for which it may be desirable toperform maintenance and/or repair on the monitored asset 101.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly or conventionally understood. As usedherein, the articles “a” and “an” refer to one or to more than one(i.e., to at least one) of the grammatical object of the article. By wayof example, “an element” means one element or more than one element.“About” and/or “approximately” as used herein when referring to ameasurable value such as an amount, a temporal duration, and the like,encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specifiedvalue, as such variations are appropriate in the context of the systems,devices, circuits, methods, and other implementations described herein.“Substantially” as used herein when referring to a measurable value suchas an amount, a temporal duration, a physical attribute (such asfrequency), and the like, also encompasses variations of ±20% or ±10%,±5%, or +0.1% from the specified value, as such variations areappropriate in the context of the systems, devices, circuits, methods,and other implementations described herein.

If implemented in-part by hardware or firmware along with software, thefunctions may be stored as one or more instructions or code on acomputer-readable medium. Examples include computer-readable mediaencoded with a data structure and computer-readable media encoded with acomputer program. Computer-readable media includes physical computerstorage media. A storage medium may be any available medium that may beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage, semiconductor storage, orother storage devices, or any other medium that may be used to storedesired program code in the form of instructions or data structures andthat may be accessed by a computer; disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

As used herein, including in the claims, “or” as used in a list of itemsprefaced by “at least one of” or “one or more of” indicates adisjunctive list such that, for example, a list of “at least one of A,B, or C” means A or B or C or AB or AC or BC or ABC (i.e., A and B andC), or combinations with more than one feature (e.g., AA, AAB, ABBC,etc.). Also, as used herein, unless otherwise stated, a statement that afunction or operation is “based on” an item or condition means that thefunction or operation is based on the stated item or condition and maybe based on one or more items and/or conditions in addition to thestated item or condition.

What is claimed is:
 1. A method for operating a sensor, the methodcomprising: sensing, at the sensor, changes in intensity of a magneticfield of a magnet affixed to a monitored asset to produce sensor data,wherein the monitored asset is disposed in a non-metallic liquid orsolid medium, and wherein the sensor is disposed outside of thenon-metallic medium; analyzing, at the sensor, the sensor data toproduce feature information indicative of vibration of the monitoredasset; and providing the feature information to a predictive algorithmto generate prognosis information indicating an occurrence of a knowncondition of the monitored asset.
 2. The method of claim 1, furthercomprising: determining a signal value based on the changes in themagnetic field; and determining a velocity of the monitored asset basedon the signal value.
 3. The method of claim 1, wherein the sensor is afirst sensor, wherein the magnet is a first magnet, wherein a secondmagnet is affixed to the monitored asset, and wherein the sensingchanges in intensity of a magnetic field of a magnet affixed to amonitored asset further comprises: sensing changes in intensity of afirst magnetic field associated with the first magnet using the firstsensor; and sensing changes in intensity of a second magnetic fieldassociated with the second magnet using a second sensor.
 4. The methodof claim 3, further comprising: determining a first electrical signalvalue based on the changes in intensity of the first magnetic field;determining a second electrical signal value based on based on changesin intensity of the second magnetic field; determining a first velocityof the monitored asset along a first axis based on the first electricalsignal value; and determining a second velocity of the monitored assetalong a second axis based on the second electrical signal value.
 5. Themethod of claim 4, further comprising: determining a phase relationshipbetween the first electrical signal value and the second electricalsignal value.
 6. The method of claim 1, further comprising: determiningan expected value for each of the one or more features over time.
 7. Themethod of claim 6, further comprising: identifying a deviation from theexpected value for a feature of the one or more features; and sendingthe feature information for the feature for which the deviation wasidentified to a server.
 8. A monitoring system comprising: a sensorconfigured to changes in intensity of a magnetic field of a magnetaffixed to a monitored asset to produce sensor data, the monitored assetis disposed in a non-metallic liquid or solid medium, the monitoringsystem is disposed outside of the non-metallic liquid or solid medium; awireless transceiver configured to transmit data to and receive datafrom a server via a communication network; and a processor configuredto: analyze the sensor data to produce feature information indicative ofvibration of the monitored asset; and provide the feature data to apredictive algorithm to generate prognosis information indicating anoccurrence of a known condition of the monitored asset.
 9. Themonitoring system of claim 8, wherein the processor is furtherconfigured to: determine a signal value based on the changes inintensity of the magnetic field; and determine a velocity of themonitored asset based on the signal value.
 10. The monitoring system ofclaim 8, wherein the sensor is a first sensor, wherein the magnet is afirst magnet, wherein a second magnet is affixed to the monitored asset,and wherein the processor being configured to sense changes in intensityof a magnetic field of a magnet affixed to a monitored asset further isfurther configured to: sense changes in intensity of a first magneticfield associated with the first magnet using the first sensor; and sensechanges in intensity of a second magnetic field associated with thesecond magnet using a second sensor.
 11. The monitoring system of claim10, further comprising: determine a first electrical signal value basedon the changes in intensity of the first magnetic field; determine asecond electrical signal value based on based on changes in intensity ofthe second magnetic field; determine a first velocity of the monitoredasset along a first axis based on the first electrical signal value; anddetermine a second velocity of the monitored asset along a second axisbased on the second electrical signal value.
 12. The monitoring systemof claim 11, wherein the processor is further configured to: determine aphase relationship between the first electrical signal value and thesecond electrical signal value.
 13. The monitoring system of claim 8,wherein the processor is further configured to: determine an expectedvalue for each of the one or more features over time.
 14. The monitoringsystem of claim 13, wherein the processor is further configured to:identify a deviation from the expected value for a feature of the one ormore features; and send the feature information for the feature forwhich the deviation was identified to a server.
 15. A non-transitory,computer-readable medium, having stored thereon computer-readableinstructions operating for operating a monitoring system, comprisinginstructions configured to cause the monitoring system to: sense changesin intensity of a magnetic field of a magnet affixed to a monitoredasset to produce sensor data, wherein the monitored asset is disposed ina non-metallic liquid or solid medium, and wherein the sensor isdisposed outside of the non-metallic medium; analyze the sensor data toproduce feature information indicative of vibration of the monitoredasset; and provide the feature data to a predictive algorithm togenerate prognosis information indicating an occurrence of a knowncondition of the monitored asset.
 16. The non-transitory,computer-readable medium of claim 15, wherein the instructionsconfigured to cause the monitoring system to determine the vibrationdata further comprise instructions configured to cause the monitoringsystem to: determine a signal value based on the changes in intensity ofthe magnetic field; and determine a velocity of the monitored assetbased on the signal value.
 17. The non-transitory, computer-readablemedium of claim 15, wherein the sensor is a first sensor, wherein themagnet is a first magnet, wherein a second magnet is affixed to themonitored asset, and wherein the instructions configured to cause themonitoring system to sense changes in intensity of a magnetic field of amagnet affixed to a monitored asset further comprise instructionsconfigured to cause the monitoring system to: sense changes in intensityof a first magnetic field associated with the first magnet using thefirst sensor; and sense changes in intensity of a second magnetic fieldassociated with the second magnet using a second sensor.
 18. Thenon-transitory, computer-readable medium of claim 17, further comprisinginstructions configured to cause the monitoring system to: determine afirst electrical signal value based on the changes in intensity of thefirst magnetic field; determine a second electrical signal value basedon based on changes in intensity of the second magnetic field; determinea first velocity of the monitored asset along a first axis based on thefirst electrical signal value; and determine a second velocity of themonitored asset along a second axis based on the second electricalsignal value.
 19. The non-transitory, computer-readable medium of claim15, further comprising instructions configured to cause the monitoringsystem to: determine a phase relationship between the first electricalsignal value and the second electrical signal value.
 20. Thenon-transitory, computer-readable medium of claim 15, further comprisinginstructions configured to cause the monitoring system to: determine anexpected value for each of the one or more features over time.
 21. Thenon-transitory, computer-readable medium of claim 20, further comprisinginstructions configured to cause the monitoring system to: identify adeviation from the expected value for a feature of the one or morefeatures; and send the feature information for the feature for which thedeviation was identified to a server.
 22. A method for operating asensor comprising: sensing changes in intensity of a magnetic field of amagnet affixed to a monitored asset to produce sensor data, wherein themonitored asset is disposed in a non-metallic liquid or solid medium,and wherein the sensor is disposed outside of the non-metallic medium;and determining vibration data for the monitored asset by analyzing thechanges in intensity of the magnetic field of the magnet associated withthe monitored asset.